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- Backhouse, Andrew, 1978
(author)
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Error-Resilient Video Coding over IP Networks
- 2005
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Licentiate thesis (other academic/artistic)abstract
- By its very nature, compressed data is more sensitive to bit corruption and data loss than uncompressed data. Transmitting data across unreliable bandlimited channels can therefore in many cases lead to an unsatisfactory reception of the source. This thesis deals with two distinct problems related to the transmission of video sequences across the Internet. The first problem addressed by this thesis, is the design of an error-resilient video coder for use on the Internet. A video coder is proposed which is based on the use of harmonic frames and leaky prediction coding. Harmonic frames are used to insert spatial redundancy into the compressed bitstream while leaky prediction coding is used to introduce temporal redundancy. The second focus of this thesis is channel prediction for the Internet. Congestion is the main cause of packet losses in the Internet. Congestion has previously been successfully predicted from measurements of the end-to-end variations in packet arrival times. Motivated by this, an algorithm is proposed to predict the probability of packet losses from the fluctuations in packet delays. The work was funded by Vinnova as part of the IPVideo project, a collaborative research project among Chalmers University of Technology, Linköping University and TopOneTech AB.
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- Backhouse, Andrew, 1978, et al.
(author)
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ML Nonlinear Smoothing for Image Segmentation and Its Relationship to The Mean Shift
- 2007
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In: IEEE International conf. on Image Processing (ICIP '07).
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Conference paper (peer-reviewed)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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- Backhouse, Andrew, 1978, et al.
(author)
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Robust Object Tracking using Particle Filters and Multi-Region Mean Shift
- 2009
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In: 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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Conference paper (peer-reviewed)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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| 9. |
- Backhouse, Andrew, 1978
(author)
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Video Signal Processing: Compression Segmentation and Tracking
- 2010
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Doctoral thesis (other academic/artistic)abstract
- This thesis considers three separate research problems within the field of video processing.The first is concerned with segmenting an image, the second with tracking an object and the third with the communication of video across an unreliable network.Segmentation is an application-specific problem. An image should be segmented to distinguish interesting regions, but it should not be segmented further. This thesis proposes an algorithm to optimally segment an image based on a maximum-likelihood criterion. The proposed algorithm is a modification of the popular mean-shift algorithm. However, unlike mean-shift, the proposed algorithmuses a model to compute the most likely image segmentation.It achieves this while preserving both the simplicity and speed of mean-shift.In this thesis two distinct methods are proposed to track objects.The first builds upon the joint anisotropic mean-shiftand particle-filter framework. This framework consists of a gradient-ascent algorithm which is seeded by a particle filter to find all likely positions, orientations and scalings of a target. We have improved this algorithm by including spatial information in the description of the target. This makes the algorithm more robust againstpartial occlusions and background clutter.The second object-tracking algorithm is an improvement to the eigenface-based tracking algorithms. These algorithms representa single appearance of a target by an interpolated NxN image and the multitude of appearances which a target can take by a linear subspace of all NxN images.It is shown that the subspace can be tracked using aKalman filter. This is a better framework for tracking as it allows motion models and appearance models to be more accurately described.The research contributions related to video communication focus on specifically on packet-based computer networks. We propose that by monitoring the variations in transmission speeds of data packets,it is possible to predict the amount of congestion in the network.This allows us to predict the probability of packet loss in such a way that adaptive compression algorithms can be designed to efficiently deal with the expected packet loss. A modified quantized frame-expansion is then proposed in this thesis for this purpose. Using a gradient-descent algorithm, optimal transforms are found for the error-resilient transmission of data. This transform has been incorporated into a novel video-compression algorithm.
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| 10. |
- Gu, Irene Yu-Hua, 1953, et al.
(author)
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Edge-Preserving Segmentation and Fusion of Medical Images by using Enhanced Mean Shift
- 2008
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In: Medicinteknikdagarna 2008, 14-15 oktober, Göteborg, Sweden.
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Conference paper (other academic/artistic)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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| 11. |
- Khan, Zulfiqar Hasan, 1976, et al.
(author)
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A Robust Particle Filter-Based Method for Tracking Single Visual Object Through Complex Scenes Using Dynamical Object Shape and Appearance Similarity
- 2011
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In: Journal of Signal Processing Systems. - : Springer Science and Business Media LLC. - 1939-8018 .- 1939-8115. ; 65:1, s. 63-79
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Journal article (peer-reviewed)abstract
- This paper addresses the issue of tracking a single visual object through crowded scenarios, where a target object may be intersected or partially occluded by other objects for a long duration, experience severe deformation and pose changes, and different motion speed in cluttered background. A robust visual object tracking scheme is proposed that exploits the dynamics of object shape and appearance similarity. The method uses a particle filter where a multi-mode anisotropic mean shift is embedded to improve the initial particles. Comparing with the conventional particle filter and mean shift-based tracking (Shan et al. 2004), our method offers the following novelties: We employ a fully tunable rectangular bounding box described by five parameters (2D central location, width, height, and orientation) and full functionaries in the joint tracking scheme; We derive the equations for the multi-mode version of the anisotropic mean shift where the rectangular bounding box is partitioned into concentric areas, allowing better tracking objects with multiple modes.The bounding box parameters are then computed by using eigen-decomposition of mean shift estimates and weighted averaging. This enables a more efficient redistributionsof initial particles towards locations associated with large weights, hence an efficient particle filter tracking using a very small number of particles (N = 15 is used). Experiments have been conducted on video containing a range of complex scenarios, where tracking results are further evaluated by using two objective criteria and compared with two existing tracking methods. Our results have shown that the propose method is robust in terms of tracking drift, tightness and accuracy of tracked bounding boxes, especially in scenarios where the target object contains long-term partial occlusions, intersections, severe deformation, pose changes, or cluttered background with similar color distributions.
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| 12. |
- Khan, Zulfiqar Hasan, 1976, et al.
(author)
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Joint Anisotropic Mean Shift and Consensus Point Feature Correspondences for Object Tracking in Video
- 2009
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In: Proc. of IEEE International conf. on Multimedia and Expo. (ICME '09). ; , s. 1270-1273
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Conference paper (peer-reviewed)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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| 13. |
- Khan, Zulfiqar Hasan, 1976, et al.
(author)
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Joint particle filters and multi-mode anisotropic mean shift for robust tracking of video objects with partitioned areas
- 2009
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In: IEEE international conf. on image processing (ICIP 2009). ; , s. 4077-4080
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Conference paper (peer-reviewed)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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| 14. |
- Khan, Zulfiqar Hasan, 1976, et al.
(author)
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Robust Visual Object Tracking using Multi-Mode Anisotropic Mean Shift and Particle Filters
- 2011
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In: IEEE Transactions on Circuits and Systems for Video Technology. - 1051-8215. ; 21:1, s. 74-87
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Journal article (peer-reviewed)abstract
- This paper addresses issues in object tracking where videos contain complex scenarios. We propose a novel tracking scheme that jointly employs particle filters and multi-mode anisotropic mean shift. The tracker estimates the dynamic shape and appearance of objects, and also performs online learning of reference object. Several partition prototypes and fully tunable parameters are applied to the rectangular object bounding box for improving the estimates of shape and multiple appearance modes in the object. The main contributions of the proposed scheme include: (a) use a novel approach for online learning of reference object distributions; (b) use a five parameter set (2D central location, width, height, and orientation) of rectangular bounding box as tunable variables in the joint tracking scheme; (c) derive the multi-mode anisotropic mean shift related to a partitioned rectangular bounding box and several partition prototypes; (d) relate the bounding box parameter computation with the multi-mode mean shift estimates by combining eigen-decomposition, geometry of subareas and weighted average. This has led to more accurate and efficient tracking where only small number of particles (
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| 15. |
- Wang, Tiesheng, 1975, et al.
(author)
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Face Tracking Using Rao-Blackwellized Particle Filter and Pose-Dependent Probabilistic PCA
- 2008
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In: Proceedings - International Conference on Image Processing, ICIP. - 1522-4880. - 9781424417643 ; , s. 853-856
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Conference paper (peer-reviewed)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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| 16. |
- Wang, Tiesheng, 1975, et al.
(author)
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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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In: IEEE International Conference on Multimedia & Expo (ICME '07), 2007.
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Conference paper (peer-reviewed)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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| 17. |
- Wang, Tiesheng, 1975, et al.
(author)
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Online subspace learning in Grassmann manifold for moving object tracking in video
- 2008
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In: IEEE international conf. Acoustics, Speech, and Signal Processing (ICASSP'08). ; , s. 4-
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Conference paper (peer-reviewed)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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