| 1. |
- Changrampadi, Mohamed Hashim, 1987, et al.
(författare)
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Multi-Class Ada-Boost Classification of Object Poses through Visual and Infrared Image Information Fusion
- 2012
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Ingår i: Proceedings - International Conference on Pattern Recognition. - 1051-4651. - 9784990644109 ; , s. 2865-2868
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Konferensbidrag (refereegranskat)abstract
- This paper presents a novel method for pose classification using fusion of visual and thermal infrared(IR) images. We propose a novel tree structure multi-class classification scheme with visual and IR sub-classifiers. These sub-classifiers are different from the conventional one-against-all or one-against-one strategies, where we handle the multi-class problem directly. We propose to use an accuracy score for the fusion of visual and IR subclassifiers. In addition, we propose to use the original Haar features plus an extra one, and a multi-threshold weak learner to obtain weak hypothesis. The experimental results on a visual and IR image dataset containing 3018 face images in three poses show that the proposed classifier achieves high classification rate of 99.50% on the test set. Comparisons are made to a fused one-vs-all method, a classifier with visual band only, and a classifier with IR band only. Results provide further support to the proposed method.
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| 2. |
- Yun, Yixiao, 1987, et al.
(författare)
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Head Pose Classification by Multi-Class AdaBoost with Fusion of RGB and Depth Images
- 2014
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Ingår i: 1st International Conference on Signal Processing and Integrated Networks (SPIN). - : IEEE. - 9781479928668 ; , s. 174-177
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Konferensbidrag (refereegranskat)abstract
- This paper addresses issues in multi-class visual object classification, where sequential learning and sensor fusionare exploited in a unified framework. We adopt a novel method for head pose classification using RGB and depth images. The main contribution of this paper is a multi-class AdaBoost classification framework where information obtained from RGB and depth modalities interactively complement each other. This is achieved by learning weak hypotheses for RGB and depth modalities independently with the same sampling weight in the boosting structure, and then fusing them through learning a sub-ensemble. Experiments are conducted on a Kinect RGB-Dface image dataset containing 4098 face images in 5 different poses. Results have shown good performance in obtaining high classification rate (99.76%) with low false alarms on the dataset.
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| 3. |
- Fu, Keren, 1988, et al.
(författare)
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Adaptive Multi-Level Region Merging for Salient Object Detection
- 2014
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Ingår i: British Machine Vision Conference (BMVC) 2014. ; , s. 11 -
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Konferensbidrag (refereegranskat)abstract
- Most existing salient object detection algorithms face the problem of either under or over-segmenting an image. More recent methods address the problem via multi-level segmentation. However, the number of segmentation levels is manually predetermined and only works well on specific class of images. In this paper, a new salient object detection scheme is presented based on adaptive multi-level region merging. A graph based merging scheme is developed to reassemble regions based on their shared contourstrength. This merging process is adaptive to complete contours of salient objects that can then be used for global perceptual analysis, e.g., foreground/ground separation. Such contour completion is enhanced by graph-based spectral decomposition. We show that even though simple region saliency measurements are adopted for each region, encouraging performance can be obtained after across-level integration. Experiments by comparing with 13 existing methods on three benchmark datasets including MSRA-1000, SOD and SED show the proposed method results in uniform object enhancement and achieves state-of-the-art performance.
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| 4. |
- Fu, Keren, 1988, et al.
(författare)
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Graph Construction for Salient Object Detection in Videos
- 2014
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Ingår i: Proceedings - International Conference on Pattern Recognition. - 1051-4651. - 9781479952083 ; , s. 2371-2376
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Konferensbidrag (refereegranskat)abstract
- Recently many graph-based salient region/object detection methods have been developed. They are rather effective for still images. However, little attention has been paid to salient region detection in videos. This paper addresses salient region detection in videos. A unified approach towards graph construction for salient object detection in videos is proposed. The proposed method combines static appearance and motion cues to construct graph, enabling a direct extension of original graph based salient region detection to video processing. To maintain coherence in both intra- and inter-frames, a spatial-temporal smoothing operation is proposed on a structured graph derived from consecutive frames. The effectiveness of the proposed method is tested and validated using seven videos from two video datasets.
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| 5. |
- Gu, Irene Yu-Hua, 1953, et al.
(författare)
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3D Limb Movement Tracking and Analysis for Neurological Dysfunctions of Neonates Using Multi-Camera Videos
- 2016
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Ingår i: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS. - 1557-170X. - 9781457702204 ; 2016-October, s. 2395-2398
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Konferensbidrag (refereegranskat)abstract
- Central nervous system dysfunction in infants may be manifested through inconsistent, rigid and abnormal limb movements. Detection of limb movement anomalies associated with such neurological dysfunctions in infants is the first step towards early treatment for improving infant development. This paper addresses the issue of detecting and quantifying limb movement anomalies in infants through non-invasive 3D image analysis methods using videos from multiple camera views. We propose a novel scheme for tracking 3D time trajectories of markers on infant’s limbs by video analysis techniques. The proposed scheme employ videos captured from three camera views. This enables us to detect a set of enhanced 3D markers through cross-view matching and to effectively handle marker self-occlusions by other body parts. We track a set of 3D trajectories of limb movements by a set of particle filters in parallel, enabling more robust 3D tracking of markers, and use the 3D model errors for quantifying abrupt limb movements. The proposed work makes a significant advancement to the previous work in [1] through employing tracking in 3D space, and hence overcome several main barriers that hinder real applications by using single camera-based techniques. To the best of our knowledge, applying such a multi-view video analysis approach for assessing neurological dysfunctions of infants through 3D time trajectories of markers on limbs is novel, and could lead to computer-aided tools for diagnosis of dysfunctions where early treatment may improve infant development. Experiments were conducted on multi-view neonate videos recorded in a clinical setting and results have provided further support to the proposed method.
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| 6. |
- Gu, Irene Yu-Hua, 1953, et al.
(författare)
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Privacy-Preserving Fall Detection in Healthcare using Shape and Motion Features from Low-Resolution RGB-D Videos
- 2016
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. - 1611-3349 .- 0302-9743. - 9783319415017 ; 9730, s. 490-499
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the issue on fall detection in healthcare using RGB-D videos. Privacy is often a major concern in video-based detection and analysis methods. We propose a video-based fall detection scheme with privacy preserving awareness. First, a set of features is defined and extracted, including local shape and shape dynamic features from object contours in depth video frames, and global appearance and motion features from HOG and HOGOF in RGB video frames. A sequence of time-dependent features is then formed by a sliding window averaging of features along the temporal direction, and use this as the input of a SVM classifier for fall detection. Separate tests were conductedon a large dataset for examining the fall detection performance with privacy-preserving awareness. These include testing the fall detection scheme that solely uses depth videos, solely uses RGB videos in different resolution, as well as the influence of individual features and feature fusion to the detection performance. Our test results show that both the dynamic shape features from depth videos and motion (HOGOF) features from low- resolution RGB videos may preserve the privacy meanwhileyield good performance (91.88% and 97.5% detection, with false alarm ≤ 1.25 %). Further, our results show that the proposed scheme is able to discriminate highly confused classes of activities (falling versus lying down) with excellent performance. Our study indicates that methods based on depth or low-resolution RGB videos may still provide effective technologies for the healthcare, without impact personnel privacy.
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| 7. |
- Kumar, Durga Priya, 1990, et al.
(författare)
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Fall detection in RGB-D videos by combining shape and motion features
- 2016
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Ingår i: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - 1520-6149. - 9781479999880 ; 2016-May, s. 1337-1341
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Konferensbidrag (refereegranskat)abstract
- This paper addresses issues in fall detection from RGB-D videos. The study focuses on measuring the dynamics of shape and motion of the target person, based on the observation that a fall usually causes drastic large shape deformation and physical movement. The main novelties include: (a) forming contours of target persons in depth images based on morphological skeleton; (b) extracting local dynamic shape and motion features from target contours; (c) encoding global shape and motion in HOG and HOGOF features from RGB images; (d) combining various shape and motion features for enhanced fall detection. Experiments have been conducted on an RGB-D video dataset for fall detection. Results show the effectiveness of the proposed method.
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| 8. |
- Yun, Yixiao, 1987, et al.
(författare)
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Exploiting Riemannian Manifolds for Daily Activity Classification in Video Towards Health Care
- 2016
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Ingår i: IEEE International Conference on E-health Networking, Application & Services (HealthCom 2016), Munich, Germany, Sept. 14-17, 2016.. - 9781509033706 ; , s. 363-368
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the problem of classifying activities of daily living in video. The proposed method uses a tree structure of two layers, where in each node of the tree there resides a Riemannian manifold that corresponds to different part-based covariance features. In the first layer, activities are classified according to the dynamics of upper body parts. In the second layer, activities are further classified according to the appearance of local image patches at hands in key frames, where the interacting objects are likely to be attached. The novelties of this paper include: (i) characterizing the motion of upper body parts by a covariance matrix of distances between each pair of key points and the orientations of lines that connect them; (ii) describing human-object interaction by the appearance of local regions around hands in key frames that are selected based on the proximity of hands to other key points; (iii) formulating a pairwise geodesics-based kernel for activity classification on Riemannian manifolds under the log-Euclidean metric. Experiments were conducted on a video dataset containing a total number of 426 video events (activities) from 4 classes. The proposed method is shown to be effective by achieving high classification accuracy (93.79% on average) and small false alarms (1.99% on average) overall, as well as for each individual class.
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| 9. |
- Yun, Yixiao, 1987, et al.
(författare)
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Fall Detection in RGB-D Videos for Elderly Care
- 2015
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Ingår i: 17th IEEE Int'l conf. on E-Health, Networking, Application & Services (HealthCom'15), 2015. - 9781467383257 ; , s. 6-
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Konferensbidrag (refereegranskat)abstract
- This paper addresses issues in fall detection from videos. Since it has been a broadly accepted intuition that a falling person usually undergoes large physical movement anddisplacement in a short time interval, the study is thus focused on measuring the intensity and temporal variation of pose change and body motion. The main novelties of this paper include: (a) characterizing pose/motion dynamics based on centroid velocity, head-to-centroid distance, histogram of oriented gradients and optical flow; (b) extracting compact features based on the mean and variance of pose/motion dynamics; (c) detecting human by combining depth information and background mixture models. Experiments have been conducted on an RGB-D video datasetfor fall detection. Tests and evaluations show the effectiveness of the proposed method.
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| 10. |
- Yun, Yixiao, 1987, et al.
(författare)
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Human Activity Recognition in Images Using SVMs and Geodesics on Smooth Manifolds
- 2014
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Ingår i: 8th ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2014; Venezia; Italy; 4 November 2014 through 7 November 2014. - New York, NY, USA : ACM. - 9781450329255 ; , s. Art. no. a20-
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Konferensbidrag (refereegranskat)abstract
- This paper addresses the problem of human activity recognition in still images. We propose a novel method that focuses on human-object interaction for feature representation of activities on Riemannian manifolds, and exploits underlying Riemannian geometry for classification. The main contributions of the paper include: (a) represent human activity by appearance features from local patches centered at hands containing interacting objects, and by structural features formed from the detected human skeleton containing the head, torso axis and hands; (b) formulate SVM kernel function based on geodesics on Riemannian manifolds under the log-Euclidean metric; (c) apply multi-class SVM classifier on the manifold under the one-against-all strategy. Experiments were conducted on a dataset containing 17196 images in 12 classes of activities from 4 subjects. Test results, evaluations, and comparisons with state-of-the-art methods provide support to the effectiveness of the proposed scheme.
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