| 1. |
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| 2. |
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| 3. |
- Axelberg, Peter G.V. 1959, et al.
(författare)
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AUTOMATIC CLASSIFICATION OF VOLTAGE EVENTS USING THE SUPPORT VECTOR MACHINE METHOD
- 2007
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Ingår i: 19th International Conference on Electricity Distribution (SIRED 2007) , Vienna, Austria, 21-24 May, 2007.
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Konferensbidrag (refereegranskat)abstract
- Statistically based classification systems need to be trained on a large number of training data in order to classify unseen data accurately. However, it is difficult to gather enough voltage events for the training purpose from real recordings. Therefore, a classification system trained to accurately classify real voltage events, but based on synthetic training data is highly in demand. This paper therefore proposes the design of a statistically based classification system trained on synthetic data. The paper gives also the results of conducted performance tests when the proposed classification system was trained to classify seven common types of voltage events. The experiments showed an overall detection rate of 81.6%, 91.9% and 99.5% respectively.
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| 4. |
- Axelberg, Peter G.V., et al.
(författare)
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Performance Tests of a Support Vector Machine used for Classification of Voltage Disturbances
- 2006
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Ingår i: in proc. of 12th International conf. on Harmonics and Quality of Power (ICHQP 2006), Cascais, Portugal, Oct.1-5, 2006.
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a novel method for classifying voltage disturbances in electric power systems by using the Support Vector Machine (SVM) method. The proposed SVM classifier is designed to classify five common types of voltage disturbances and experiments have been conducted on recorded disturbances with good classification results. The proposed SVM classifier is also shown to be robust in terms of using training data and testing data that originate from two different power networks.
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| 5. |
- Axelberg, Peter G.V. 1959, et al.
(författare)
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Support Vector Machine for Classification of Voltage Disturbances
- 2007
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Ingår i: accepted for publication in IEEE Transactions on Power Delivery. ; 22:3, s. 1297-1303, July, 2007
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Tidskriftsartikel (refereegranskat)abstract
- The Support Vector Machine (SVM) is a powerful method for statistical classification of data used in a number of different applications. However, the usefulness of the method in a commercial available system is very much dependent on whether the SVM classifier can be pre-trained from a factory since it is not realistic that the SVM classifier must be trained by the customers themselves before it can be used. We first propose a novel SVM classification system for voltage disturbances. Our aim also includes investigating the performance of the proposed SVM classifier when the voltage disturbance data used for training and testing are originated from different sources. The data used in the experiments were originated from both real disturbances recorded in two different power networks and from synthetic data. The experimental results have shown excellent accuracy in classification when training data were originated from one power network and unseen testing data from another. High accuracy was also achieved when the SVM classifier was trained on data from a real power network and test data originated from synthetic data. Slightly less accuracy was achieved when the SVM classifier was trained on synthetic data and test data were originated from the power network.
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| 6. |
- Axelberg, Peter G.V. 1959, et al.
(författare)
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Trace of flicker sources by using the quantity of flicker power
- 2007
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Ingår i: IEEE transactions on Power Delivery. ; 23:1, s. pp.465-471
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Tidskriftsartikel (refereegranskat)abstract
- Industries that produce flicker are often placed close to each other and connected to the same power grid system. This implies that the measured flicker level at the point of common coupling (PCC) is a result of contribution from a number of different flicker sources. In a mitigation process it is essential to know which one of the flicker sources is the dominant one. We propose a method to determine the flicker propagations and trace the flicker sources by using flicker power measurements. Flicker power is considered as a quantity containing both sign and magnitude. The sign determines if a flicker source is placed downstream or upstream with respect to a given monitoring point and the magnitude is used to determine the propagation of flicker power throughout the power network and to trace the dominant flicker source. This paper covers the theoretical background of flicker power and describes a novel method for calculation of flicker power that can be implemented in a power network analyzer. Also conducted simulations and a field test based on the proposed method will be described in the paper.
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| 7. |
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| 8. |
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| 9. |
- Backhouse, Andrew, 1978, et al.
(författare)
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ML Nonlinear Smoothing for Image Segmentation and Its Relationship to The Mean Shift
- 2007
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Ingår i: IEEE International conf. on Image Processing (ICIP '07).
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the issues of nonlinear edge-preserving image smoothing and segmentation. A ML-based approach is proposed which uses an iterative algorithm to solve the problem. First, assumptions about segments are made by describing the joint probability distribution of pixel positions and colours within segments. Based on these assumptions, an optimal smoothing algorithm is derived under the ML condition. By studying the derived algorithm, we show that the solution is related to a two-stage mean shift which is separated in space and range. This novel ML-based approach takes a new kernel function. Experiments have been conducted on a range of images to smooth and segment them. Visual results and evaluations with 2 objective criteria have shown that the proposed method has led to improved results which suffer from less over-segmentation than the standard mean-shift.
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| 10. |
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| 11. |
- Backhouse, Andrew, 1978, et al.
(författare)
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Robust Object Tracking using Particle Filters and Multi-Region Mean Shift
- 2009
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Berlin, Heidelberg : Springer Berlin Heidelberg. - 1611-3349 .- 0302-9743. - 9783642104664 ; 5879, s. 11-403
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Konferensbidrag (refereegranskat)abstract
- In this paper, we introduce a novel algorithm which buildsupon the combined anisotropic mean-shift and particle filter framework. The anisotropic mean-shift with 5 degrees of freedom, is extended to work on a partition of the object into concentric rings. This adds spatial information to the description of the object which makes the algorithm more resilient to occlusion and less susceptible to confusion with objects having similar color densities. Experiments conducted on videos containing deformable objects with long-term partial occlusion (or, short-term full occlusion) and intersection have shown robust tracking performance, especially in tracking objects with long term partial occlusion, short term full occlusion, close color background clutter, severe object deformation and fast changing motion. Comparisons with two existing methods have shown marked improvement in terms of robustness to occlusions, tightness and accuracy of tracked box, and tracking drifts.
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| 12. |
- Berlijn, Sonja M., et al.
(författare)
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Laboratory Tests and Web Based Surveillance to Determine the Ice- and Snow Performance of Insulators
- 2007
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Ingår i: IEEE Transactions on Dielectrics and Electrical Insulation, Special Issue on Flashover of Ice or Snow-Covered Insulators. ; 14:6, s. 1373-1380, 2007
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Tidskriftsartikel (refereegranskat)abstract
- To be able to determine, verify and monitor the ice- and snow performance of different insulation solutions, laboratory test methods and an on-site, on-line web based surveillance system are needed. The method for determining the ice performance in laboratory conditions, Ice Progressive Stress (IPS) method, is described in this paper. Further it is described how to use the results of this type of test to estimate statistically the performance of complete overhead line equipped by different insulators. To verify the laboratory ice- and snow test method, to get an idea about the type and number of ice and snow events actually occurring in service and to get more information about ice and snow phenomena in real life an on-site on-line web based surveillance system was designed and built. This sophisticated system, including the automatic image analysis and used statistical tools is described in this paper. Besides the description of the laboratory test method and the surveillance system, service experience, pictures and interesting results obtained so far are also presented.
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| 13. |
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| 14. |
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| 15. |
- Bollen, Math, et al.
(författare)
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Classification of Underlying Causes of Power Quality Disturbances: Deterministic versus Statistical Methods
- 2007
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Ingår i: Eurasip Journal on Applied Signal Processing. - : Springer Science and Business Media LLC. - 1110-8657 .- 1687-0433. ; 2007, s. 17 pages (Article ID 79747)-
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents the two main types of classification methods for power quality disturbances based on underlying causes: deterministic classification, giving an expert system as an example, and statistical classification, with support vector machines as an example. An expert system is suitable when one has limited amount of data and sufficient power system expert knowledge, however its application requires a set of threshold values. Statistical methods are suitable when large amount of data is available for training. Two important issues to guarantee the effectiveness of a classifier, data segmentation and featureextraction, are discussed. Segmentation of a sequence of data recording is pre-processing to partition the datainto segments each representing a duration containing either an event or transition between two events. Extraction of features is applied to each segment individually. Some useful features and their effectiveness are then discussed. Some experimental results are included for demonstrating theeffectiveness of both systems. Finally, conclusions are given together with the discussion of some future research directions.
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| 16. |
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| 17. |
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| 18. |
- Bollen, Math H.J., et al.
(författare)
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On the Analysis of Voltage and Current Transients in Three-Phase Power Systems
- 2007
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Ingår i: IEEE Transactions on Power Delivery. ; 22:2, s. 1194-1201, April 2007
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a method for analysis measurementsof voltage transients in three-phase systems. The method is based on the Clarke transform introduced in 1950 forcalculations of travelling waves along three-phase transmission lines. The proposed method also shows close similarities with the classification of three-phase unbalanced voltage dips into types C and D. After extracting the actual transient, e.g. by using a notch filter centered on the power-system frequency, the three signals are decomposed into seven components. From the relationbetween these seven components, the dominant component isidentified. The method is successfully applied to a number of measured transients. The paper also identifies the limitations of the method and gives suggestions for future work.
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| 19. |
- Bollen, Math H. J., et al.
(författare)
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Signal processing of power-quality disturbances
- 2006
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Ingår i: Johh Wiley & Sons - IEEE Press. - Piscataway, NJ : IEEE Communications Society. - 0471731684 ; , s. 888-
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Bok (populärvet., debatt m.m.)abstract
- Bridging the gap between power quality and signal processing This innovative new text brings together two leading experts, one from signal processing and the other from power quality. Combining their fields of expertise, they set forth and investigate various types of power quality disturbances, how measurements of these disturbances are processed and interpreted, and, finally, the use and interpretation of power quality standards documents. As a practical aid to readers, the authors make a clear distinction between two types of power quality disturbances: Variations: disturbances that are continuously present Events: disturbances that occur occasionally A complete analysis and full set of tools are provided for each type of disturbance: Detailed examination of the origin of the disturbance Signal processing measurement techniques, including advanced techniques and those techniques set forth in standards documents Interpretation and analysis of measurement data Methods for further processing the features extracted from the signal processing into site and system indices The depth of coverage is outstanding: the authors present and analyze material that is not covered in the standards nor found in the scientific literature. This text is intended for two groups of readers: students and researchers in power engineering who need to use signal processing techniques for power system applications, and students and researchers in signal processing who need to perform power system disturbance analyses and diagnostics. It is also highly recommended for any engineer or utility professional involved in power quality monitoring.
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| 20. |
- Feng, S, et al.
(författare)
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3D Face Recognition Using Affine Integral Invariants
- 2006
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Ingår i: IEEE international conf. on ASSP (ICASSP-06). ; II, s. 189-192
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Konferensbidrag (refereegranskat)abstract
- A new 3D face representation and recognition approach is presented in this paper. Two sets of facial curves are extracted from a face range image, and a novel facial feature representation, the affine integral invariant, is introduced to mitigate the effect of pose on the facial curves. A human face is shown to be representable by a smallsubset of those affine integral invariant curves. A recognition procedure based on the Discriminant Analysis and Jensen-Shannon Divergence analysis is proposed. Substantiating examples are provided with an achieved classification accuracy of 92.57%.
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| 21. |
- Gu, Irene Yu-Hua, 1953, et al.
(författare)
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Automatic Classification of Wood Defects using Support Vector Machines
- 2008
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Berlin, Heidelberg : Springer Berlin Heidelberg. - 1611-3349 .- 0302-9743. - 3642023444 ; 5337, s. 356-367
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the issue of automatic wood defect classification. We propose a tree-structure support vector machine (SVM) to classify four types of wood knots by using images captured from lumber boards. Simple and effective features are proposed and extracted by first partitioning the knot images into 3 distinct areas, followed by applying an order statistic filter to yield an average pseudo color feature in each area. Excellent results have been obtained for the proposed SVM classifier that is trained by 800 wood knot images. Performance evaluation has shown that the proposed SVM classifier has resulted in an average classification rate of 96.5% and false alarm rate of 2.25% over 400 test knot images. Our future work includes more extensive tests on large data set and the extension of knot types.
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| 22. |
- Gu, Irene Yu-Hua, 1953, et al.
(författare)
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Automatic Surveillance and Analysis of Snow and Ice Coverage on Electrical Insulators of Power Transmission Lines
- 2008
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Berlin, Heidelberg : Springer Berlin Heidelberg. - 1611-3349 .- 0302-9743. - 9783642023446 ; 5337, s. 368-379
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Konferensbidrag (refereegranskat)abstract
- One of the large problems for electrical power delivery through power lines in the Northern countries is when snow or ice accumulates on electrical insulators. This could lead to snow or ice-induced outages and voltage collapse, causing huge economic loss. This paper proposes a novel real-time automatic surveillance and image analysis system for detecting and estimating the snow and ice coverage on electric insulators using images captured from an outdoor 420 kV power transmission line. In addition, the swing angle of insulators is estimated, as large swing angles due to wind cause short circuits. Hybrid techniques by combining histogram, edges, boundaries and cross-correlations are employed for handling a broad range of scenarios caused by changing weather and lighting conditions. Experiments have been conducted on the captured images over several month periods. Results have shown that the proposed system has provided valuable estimation results. For image pixels related to snows on the insulator, the current system has yielded an average detection rate of 93% for good quality images, and 67.6% for images containing large amount of poor quality ones, and the corresponding average false alarm ranges from 9% to 18.1%. Further improvement may be achieved by using video-based analysis and improved camera settings.
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| 23. |
- Gu, Irene Yu-Hua, 1953, et al.
(författare)
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Edge-Preserving Segmentation and Fusion of Medical Images by using Enhanced Mean Shift
- 2008
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Ingår i: Medicinteknikdagarna 2008, 14-15 oktober, Göteborg, Sweden.
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Konferensbidrag (övrigt vetenskapligt/konstnärligt)abstract
- This paper addresses the issue of medical image segmentation by using an enhanced spatial-range mean shift. Mean shift is a method for estimating local modes (maxima) of pdf (probability density function) using a kernel-based approach.This paper describes an enhanced spatial-range mean shift segmentation method for biomedical (MRI) image segmentation. Preliminary work and the results on fusion of segmented brain images from different sensors (e.g. MRI, CT) are presented and discussed.
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| 24. |
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| 25. |
- Gu, Irene Yu-Hua, 1953, et al.
(författare)
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Intelligent Video Surveillance for Detecting Snow and Ice Coverage on Electrical Insulators of Power Transmission Lines
- 2009
-
Ingår i: Springer LNCS (for CAIP'09). ; 5702, s. 1179-1187
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Konferensbidrag (refereegranskat)abstract
- One of the problems for electrical power delivery throughpower lines in northern countries is when snow or ice accumulates on electrical insulators. This could lead to snow or ice-induced outages and voltage collapse, causing huge economic loss. This paper proposes a novel real-time intelligent surveillance and image analysis system for detecting and estimating the snow and ice coverage on electric insulators using images captured from an outdoor 420 kV power transmission line. In addition, the swing angle of insulators is estimated, as large swing angles due to wind cause short circuits. Hybrid techniques by combining histogram, edges, boundaries and cross correlations are employed for handling a broad range of scenarios caused by changing weather and lighting conditions. Experiments have been conducted on the captured images over several month periods. Results have shown that the proposed system has provided valuable estimation results. For image pixels related to snows on the insulator, the current system has yielded an averagedetection rate of 93% for good quality images, and 67.6% for images containing large amount of poor quality ones, and the corresponding average false alarm ranges from 9% to 18.1%. Further improvement may be achieved by using video-based analysis and improved camera settings.
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| 26. |
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| 27. |
- Gu, Irene Yu-Hua, 1953, et al.
(författare)
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Online Detection of Snow Coverage and Swing Angles of Electrical Insulators on Power Transmission Lines Using Videos
- 2009
-
Ingår i: IEEE international conf. on image processing (ICIP 2009), Cairo, EGYPT, NOV 07-10, 2009. - 9781424456536 ; 1-6, s. 3213-3216
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Konferensbidrag (refereegranskat)abstract
- A potential fatal problem for electrical power delivery through power lines in Northern countries is when snow or ice accumulates on electrical insulators. This could lead to snow or ice-induced outages and voltage collapse, causing huge economic loss. Further, large swing angles due to wind may cause short circuits. This paper presents a novel video surveillance system for detecting snow coverage on electric insulators and swing angles of insulators using videos from a remote outdoor 420 kV power transmission line. To the best of our knowledge, it is the first insulator snow surveillance system base on automatic image analysis techniques. We propose using hybrid techniques by combining histograms, boundaries and template cross-correlations for analyzing a broad range of scenarios caused by changing weather and lighting conditions. Experiments on videos captured during several monthperiods have shown promising and valuable estimation results. For image pixels related to snow on insulators, our system has yielded an average detection rate of 93% for good quality images and 67.6% for poor quality images, and a corresponding average false alarm of 9% and 18.1%.
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| 28. |
- Gu, Irene Yu-Hua, 1953, et al.
(författare)
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Parameter Estimation of Multidimensional NMR Signals based on High-Resolution Subband Analysis of 2D NMR Projections
- 2009
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Ingår i: IEEE International Conf. Acoustics, Speech and Signal Processing (ICASSP 2009). ; , s. 497-500
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Konferensbidrag (refereegranskat)abstract
- NMR spectroscopy is a powerful technique used in protein research for comprehensive functional characterizations, e.g. structure determination at atomic resolution. Due to the molecular size (typically>1000 atoms), proteinNMR spectra contain a large number of signal frequencies. Resolving these requires high-dimensional spectroscopy. However, when the number of frequency exceeds three, conventional approaches start to demand unrealistic long experiment time, and the data analysis becomes challenging. In this paper we explore a combination of novel methods: Data from 5D NMR experiments are recorded as a series of 2D projections, which are then subjected to 2D subband filters and 2D LS-ESPRIT for estimation of signal parameters. Based on the relations established between 5D NMR signals and their 2D counterparts, projection spectroscopy allows to extract highly similar information as what would be found in conventional 5D NMR experiments; however, while the latter would require months of experiment time, the recording of all necessary projections can be accomplished within 1-2 days. Preliminary results show the efficiency of the method with respect to accuracy and resolution of the parameter estimates as compared with conventional methods.
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| 29. |
- Gu, Irene Yu-Hua, 1953, et al.
(författare)
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Video Segmentation using Joint Space-Time-Range Adaptive Mean Shift
- 2006
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Ingår i: To appear in "Advances in Multimedia Information Processing - PCM 2006", LNCS Vol. 4261, Springer, 2006. ; 4261
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Konferensbidrag (refereegranskat)abstract
- Video segmentation has drawn increasing interest in multimedia applications. This paper proposes a novel joint space-time-range domain adaptive mean shift filter for video segmentation. In the proposed method, segmentation of moving/static objects/background is obtained through inter-frame mode-matching in consecutive frames and motion vector mode estimation. Newly appearing objects/regions in the current frame due to new foreground objects or uncovered background regions are segmented by intra-frame mode estimation. Simulations have been conducted to several image sequences, and results have shown the effectiveness and robustness of the proposed method. Further study is continued to evaluate the results.
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| 30. |
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| 31. |
- Hur, Kyeon, et al.
(författare)
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On the Empirical Estimation of Utility Distribution Damping Parameters using Power Quality Waveform Data
- 2007
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Ingår i: Eurasip Journal on Applied Signal Processing. - : Springer Science and Business Media LLC. - 1110-8657 .- 1687-0433. ; 2007, s. 12 pages (Article ID 95328)-
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Tidskriftsartikel (refereegranskat)abstract
- Efficient yet accurate methodology for estimating the system damping is described in this paper. The proposed technique is based on the linear dynamic system theory and the Hilbert damping analysis. The proposed technique requires capacitor switching waveforms only. The detected envelope of the intrinsic transient portion of the voltage waveform after capacitor bank energizing and its decay rate along with the damped resonant frequency can quantify effective X/R ratio of the system. Thus, the proposed method provides complete knowledge of system impedance characteristics. The estimated system damping can also be used to evaluate the system vulnerability to various PQ disturbances, particularly, resonance phenomena, so that the utility may take preventive measures and improve PQ the system.
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| 32. |
- Khan, Zulfiqar Hasan, 1976, et al.
(författare)
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Joint Anisotropic Mean Shift and Consensus Point Feature Correspondences for Object Tracking in Video
- 2009
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Ingår i: Proc. of IEEE International conf. on Multimedia and Expo. (ICME '09). ; , s. 1270-1273
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Konferensbidrag (refereegranskat)abstract
- We propose a novel tracking scheme that jointly employs point feature correspondences and object appearance similarity. For selecting point correspondences, we use a subset of scale-invariant point features from SIFT that agree with a pre-defined affine transformation. The selected consensus points are then used for pre-selecting candidate regions. For appearance similarity based tracking, we employ an existing anisotropic mean shift, from which the formula for estimating bounding box parameters (width, height, orientation and center) are derived. A switching criterion is utilized to handle the situation where only a small number of point correspondences is found. Experiments and evaluation are performed on tracking moving objects on videos where objects may contain partial occlusions, intersection, deformation and pose changes among other transforms. Our comparisons with two existing methods have shown that the proposed scheme has yielded marked improvement, especially in terms of reducing tracking drifts, of robustness to occlusions, and of tightness and accuracy of tracked bounding box.
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| 33. |
- Khan, Zulfiqar Hasan, 1976, et al.
(författare)
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Joint particle filters and multi-mode anisotropic mean shift for robust tracking of video objects with partitioned areas
- 2009
-
Ingår i: IEEE international conf. on image processing (ICIP 2009). ; , s. 4077-4080
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Konferensbidrag (refereegranskat)abstract
- We propose a novel scheme that jointly employs anisotropic mean shift and particle filters for tracking moving objects from video. The proposed anisotropic mean shift, that is applied to partitioned areas in a candidate object bounding box whose parameters (center, width, height and orientation) are adjusted during the mean shift iterations, seeks multiple local modes in spatial-kernel weighted color histograms. By using a Gaussian distributed Bhattacharyya distance as the likelihood and mean shift updated parameters as the state vector, particle filters become more efficient in terms of tracking using a small number of particles (
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| 34. |
- Li, Liyuan, et al.
(författare)
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An Efficient Sequential Approach to Tracking Multiple Objects through Crowds for Real-Time Intelligent CCTV Systems
- 2008
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Ingår i: IEEE Transactions on Systems, Man and Cybernetics Part B: Cybernetics. - 1083-4419. ; 38:5, s. 1254-1269
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Tidskriftsartikel (refereegranskat)abstract
- Efficiency and robustness are the two most important issues for multi-object tracking algorithms in real-time intelligent video surveillance systems. We propose a novel2 1/2 D approach to real time multi-object tracking in crowds, which is formulated as a MAP estimation problem and is approximated through an “assignment” step and a “location” step. Observing that the occluding object is usually less affected by the occluded objects, sequential solutions for the assignment and the location are derived. A novel dominant color histogram (DCH) is proposed as an efficient object model. The DCH can be regarded as a generalized color histogram, where dominant colors are selected based on a given distance measure. Comparing with conventional color histograms, DCH only requires a few color components (31 in average). Further, our theoretical analysis and evaluation on real data have shown that DCHs are robust to illumination changes. Using DCH, efficient implementations of sequential solutions for the assignment and the location steps are proposed. The "Assignment" step includes the estimation of depth order for the objects in a dispersing group, one-by-one assignment, and feature exclusion from the group representation. The "Location" step includes the depth order estimation for the objects in a new group, two-phase mean-shift location, and the exclusion of tracked objects from the new position in the group. Multi-object tracking results and evaluation from public datasets are presented. Experiments on image sequences captured from crowded public environments have shown good tracking results, where about 90% of objects have been successfully tracked with the correct identification numbers by the proposed method. Our results and evaluation have indicated that the method is efficient and robust for tracking multiple objects (large than or equal to 3) in complex occlusions for real world surveillance scenarios.
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| 35. |
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| 36. |
- Luo, Ruijiang, et al.
(författare)
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Efficient Adaptive Background Subtraction based on Multi-Resolution Background Modelling and Updating
- 2007
-
Ingår i: to appear in Proc. of Pacific-RIM Conf. on Multimedia (PCM'07), Dec. 11-14, Hong Kong, 2007.
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Konferensbidrag (refereegranskat)abstract
- Adaptive background subtraction (ABS) is a fundamental step for foreground object detection in many real-time video surveillance systems. In many ABS methods, a pixel-based statistical model is used for the background and each pixel is updated online to adapt to various background changes. As a result, heavy computation and memory consumption are required. In this paper, we propose an efficient methodology for implementation of ABS algorithms based on multi-resolution background modelling and sequential sampling for updating background. Experiments and quantitative evaluation are conducted on two open data sets (PETS2001 and PETS2006) and scenarios captured in some public places, and some results are included. Our results have shown that the proposed method requires a significant reduction in memory and CPU usage, meanwhile maintaining a similar foreground segmentation performance as compared with the corresponding single resolution methods.
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| 37. |
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| 38. |
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| 39. |
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| 40. |
- Strandmark, Petter, et al.
(författare)
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Joint Random Sample Consensus and Multiple Motion Models for Robust Video Tracking
- 2009
-
Ingår i: Lecture Notes in Computer Science. - Berlin, Heidelberg : Springer Berlin Heidelberg. - 1611-3349 .- 0302-9743. ; 5575/2009, s. 450-459:5575, s. 450-459
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Konferensbidrag (refereegranskat)abstract
- We present a novel method for tracking multiple objects in video captured by a non-stationary camera. For low quality video, RANSAC estimation fails when the number of good matches shrinks below the minimum required to estimate the motion model. This paper extends RANSAC in the following ways: (a) Allowing multiple models of different complexity to be chosen at random; (b) Introducing a conditional probability to measure the suitability of each transformation candidate, given the object locations in previous frames; (c) Determining the best suitable transformation by the number of consensus points, the probability and the model complexity. Our experimental results have shown that the proposed estimation method better handles video of low quality and that it is able to track deformable objects with pose changes, occlusions, motion blur and overlap. We also show that using multiple models of increasing complexity is more effective than just using RANSAC with the complex model only.
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| 41. |
- Tjäder, A., et al.
(författare)
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Performance Evaluation for Frequency Estimation of Transients Using the ESPRIT: Measured Noise versus White Noise
- 2008
-
Ingår i: ICHQP 2008: 13th International Conference on Harmonics and Quality of Power; Wollongong, NSW. - Piscataway, NJ : IEEE Communications Society. ; , s. 8-
-
Konferensbidrag (refereegranskat)abstract
- A number of papers have proposed to use the ESPRIT method for estimating some power system transients. The mathematical model for using the ESPRIT requires that the additive noise be white. Fail to satisfy this could lead to degraded performance of frequency estimation. However, in practical situations the noise is often non-white. In this paper we study the performance of the ESPRIT frequency estimation method when the additive noise is measured from power systems. It is shown that tests using synthetic white noise are not sufficient for realistic power-system scenarios, as the noise is often non-white and not i.i.d. distributed. First, a set of measured voltage waveforms is superimposed to synthetic transients with known frequencies. This yields a set of semi-measured transients. The ESPRIT method is then applied to estimate the frequencies. The performance of the ESPRIT is then evaluated in term of the mean value and standard deviation. Our experiments show that, for a transient contains one sinusoid, the ESPRIT of sinusoidal model order K=2 results in an acceptable accuracy if the amplitude of the transient is well above the noise level and the transient frequency is not too close to the power-system frequency. This is sufficient for most practical applications. A better performance for higher noise levels is obtained by significantly increasing the sinusoidal model order in the ESPRIT.
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| 42. |
- Wang, Tiesheng, 1975, et al.
(författare)
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Adaptive Particle Filters for Visual Object Tracking using Joint PCA Appearance Model and Consensus Point Correspondences
- 2009
-
Ingår i: Proc. of IEEE International conf. on Multimedia and Expo. (ICME '09). - 1945-7871. - 9781424442904 ; , s. 1370-1373
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Konferensbidrag (refereegranskat)abstract
- This paper addresses issues on moving object tracking fromvideos. We propose a novel tracking scheme that jointly exploits local object features using consensus point correspondences, and global object appearance and shape models using adaptive particle filter-based eigen-tracking. The paper include the following main novelties: (a) employ consensus feature point correspondences to estimate the motion vector of shape model; (b) employ adaptive particle filters and motion-corrected state vector for joint appearance- and shape-based eigen-tracking. An adaptive number of particles is chosen automatically based on an updated estimation of covariance matrix. Further, online learning is made adaptive to avoid learning using partially-occluded objects. The proposed scheme is realized by integrating SURF and RANSAC for estimating consensus point correspondences, and modify an existing particle filter-based eigen-tracking. Experimental results on tracking moving objects in videos have shown that the proposed scheme provides more accurate tracking, especially for objects with fast motion or long-term partial occlusions. The average number of particles is significantly reduced. Comparisons have been made with an existing method, results have shown that the proposed scheme has provided an improved tracking accuracy at the cost of more computations.
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| 43. |
- Wang, Tiesheng, 1975, et al.
(författare)
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Enhanced Landmine Detection From Low Resolution IR Image Sequences
- 2009
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Ingår i: Springer LNCS (for CAIP'09). ; 5702, s. 1236-1244
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Konferensbidrag (refereegranskat)abstract
- We deal with the problem of landmine field detection usinglow-resolution infrared (IR) image sequences measured from airborne or vehicle-borne passive IR cameras. The proposed scheme contains two parts: a) employ a multi-scale detector, i.e., a special type of isotropic bandpass filters, to detect landmine candidates in each frame; b) enhance landmine detection through seeking maximum consensus of corresponding landmine candidates over image frames. Experiments were conducted on several IR image sequences measured from airborne and vehicle-borne cameras, where some results are included. As shown in our experiments, the landmine signatures have been significantly enhanced using the proposed scheme, and automatic detection results are reasonably good. These methods can therefore be applied to assisting humanitarian demining work for landmine field detection.
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| 44. |
- Wang, Tiesheng, 1975, et al.
(författare)
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Face Tracking Using Rao-Blackwellized Particle Filter and Pose-Dependent Probabilistic PCA
- 2008
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Ingår i: Proceedings - International Conference on Image Processing, ICIP. - 1522-4880. - 9781424417643 ; , s. 853-856
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Konferensbidrag (refereegranskat)abstract
- This paper deals with face blob tracking, where face undergoes various pose changes. We propose a novel trackingmethod to deal with face pose changes during tracking. In the method, tracking is formulated as an approximate solution to the MAP estimate of state vector, consisting of a linear and a nonlinear part. Multi-pose face appearance is modeled by locally linear models, and estimated by the probabilistic PCA for individual pose combined with a Markov model for pose changes. Shape and locations of face blobs and pose index are assumed to be nonlinear and estimated by Rao-Blackwellized particle filters (RBPF), which also enables separate estimation of linear state vector through marginalizing the joint probability. The proposed method has been tested for videos containing frequent face pose changes and large illumination variations, under 5 pose models (left, frontal, right, up, down), and the tracking results are shown to be robust to varying speed pose changes and with relatively tight boxes.
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| 45. |
- Wang, Tiesheng, 1975, et al.
(författare)
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Moving Object Tracking from Videos based on Enhanced Space-Time-Range Mean Shift and Motion Consistency
- 2007
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Ingår i: IEEE International Conference on Multimedia & Expo (ICME '07), 2007.
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Konferensbidrag (refereegranskat)abstract
- Video surveillance and object tracking have drawn increasedinterests in recent years. This paper addresses the problem of moving object tracking from image sequences captured fromstationary cameras. Based on our previous work on videosegmentation using joint space-time-range mean shift, we extend the scheme to enable the tracking of moving objects. Large displacements of pdf modes in consecutive image frames are exploited for tracking. We also improve the above mean shift-based video segmentation by introducing edge-guided merging of over-segmented regions. This can be viewed as an extension of the enhanced mean shift 2D image segmentation to the enhanced space-time-range mean shift video segmentation. Experiments have been conducted on several indoor and outdoor videos. Our preliminary results and performance evaluation have indicated the effectiveness of the proposed scheme.
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| 46. |
- Wang, Tiesheng, 1975, et al.
(författare)
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Object Tracking using Incremental 2D-PCA Learning and ML Eestimation
- 2007
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Ingår i: IEEE International Conference on Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. - 1520-6149. - 1424407273 ; 1
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Konferensbidrag (refereegranskat)abstract
- Video surveillance has drawn increasing interests in recent years. This paper addresses the issue of moving object tracking from videos. A two-step processing procedure is proposed: an incremental 2DPCA (two-dimensional PrincipalComponent Analysis)-based method for characterizing objectsgiven the tracked regions, and a ML (Maximum Likelihood)blob-tracking process given the object characterizationand the previous blob sequence. The proposed incremental2DPCA updates the row- and column-projected covariancematrices recursively, and is computationally more efficient for online learning of dynamic objects. The proposed ML blobtracking takes into account both the shape information and object characteristics. Tests and evaluations were performed on indoor and outdoor image sequences containing a range of moving objects in dynamic backgrounds, which have shown good tracking results. Comparisons with the method using the conventional PCA were also made.
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| 47. |
- Wang, Tiesheng, 1975, et al.
(författare)
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Online subspace learning in Grassmann manifold for moving object tracking in video
- 2008
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Ingår i: IEEE international conf. Acoustics, Speech, and Signal Processing (ICASSP'08). ; , s. 4-
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Konferensbidrag (refereegranskat)abstract
- This paper proposes a robust object tracking method in video where the time-varying principal components of object’s appearance are updated online. Instead of directly updating the PCA-based subspace using matrix decomposition, the subspace is updated by tracking on the Grassmann manifold. The object tracker performs two alternating processes: (a) online learning of principal component subspace; (b) tracking a moving object using the learned subspace and a particle filter. Learning a PCA-based subspace is performed by treating principal component decompositions as noisy measurements. The measurements are mapped onto the Lie algebra of the Grassmann manifold. The direction of movement of the subspace is then tracked in the Lie algebra using a Kalman filter. The filtered output is then mapped back onto the Grassmann surface to update the principal component-based subspace. This produces a more reliable learning of the subspace. Experiments have been conducted on face image sequences where heads were tilted in variable speed, partial face occlusion, along with changes in object depth and in illuminations. The results and evaluations have shown that the proposed method is robust against these changes when tracking moving objects.
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| 48. |
- Wang, Tiesheng, 1975, et al.
(författare)
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Tracking moving objects in video using enhanced mean shift and region-based motion field
- 2007
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Ingår i: proc. of European Signal Processing Conference ( EUSIPCO '07), Sept. 2007, Poland.
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Konferensbidrag (refereegranskat)abstract
- In this paper, we propose a scheme for moving object tracking from videos by combining mean shift and motion field statistics. For mean shift, we employ an enhanced spatial-range mean shift that enables a reduced number of oversegmentation. For motion statistics, we combine the optical flow and high-order moment to generate motion regions that are associated with moving objects (or object parts). Experiments have been conducted on several indoor and outdoor (color/gray-scale) image sequences ranging from simple to median complexity. To evaluate the performance, three objective criteria are applied in addition to the visual inspection. The results show that the proposed method is promising for moving object tracking in video, with an averaging detection rate of 95%. Further, the proposed scheme is compared with that using the conventional mean shift for the tracking, indicating a significantly reduction in false alarm (≈ 30%).
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| 49. |
- Xu, Zhifei, et al.
(författare)
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An Eigenbackground Subtraction Method using Recursive Error Compensation
- 2006
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Ingår i: to appear in "Advances in Multimedia Information Processing - PCM 2006", LNCS (Springer) Vol. 4261, 2006. ; 4261
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Konferensbidrag (refereegranskat)abstract
- Eigenbackground subtraction is a common method for movingobject detection. The method uses the difference between input image and the reconstructed background image to detect foreground objects based on eigenvalue decomposition. In the method, foreground regions are represented in the reconstructed image using eigenbackground. This results in errors that are spread out over the entire reconstructed reference image. This will also result in degradation of quality of reconstructed background leading to inaccurate moving object detection. In order to compensate these regions, an efficient recursive error compensation method is proposed. The experimental results show that a better approximation of the background is constructed by the proposed method and more accurate foreground objects can be detected based on the reconstructed background.
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| 50. |
- Xu, Zhifei, et al.
(författare)
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Recursive Error-Compensated Dynamic Eigenbackground Learning and Adptive Background Subtraction in Video
- 2008
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Ingår i: Optical Engineering. - : SPIE-Intl Soc Optical Eng. - 1560-2303 .- 0091-3286. ; 47:5, s. 11 pages (article No. 057001)-
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Tidskriftsartikel (refereegranskat)abstract
- We address the problem of foreground object detection through background subtraction. Although eigenbackground models are successful in many computer vision applications, background subtraction methods based on a conventional eigenbackground method may suffer from high false-alarm rates in the foreground detection due to possible absorption of foreground changes into the eigenbackgroundmodel. This paper introduces an improved eigenbackground modeling method for videos by recursively applying an error compensation process to reduce the influence of foreground moving objects on the eigenbackground model. An adaptive threshold method is also introduced for background subtraction, where the threshold is determined by combininga fixed global threshold and a variable local threshold. A fast algorithm is then given as an approximation to the proposed method by imposing and exploiting a constraint on motion consistency, leading to about 50% reduction in computations. Experiments have been performed on a rangeof videos with satisfactory results. Performance is evaluated using an objective criterion. Comparisons are made with two existing methods.
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