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IEEE Transactions on Pattern Analysis and Machine Intelligence
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An introduction to signal detection and estimation (2nd ed.)
Robust computation of optical flow in a multi-scale differential framework
International Journal of Computer Vision
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Real-time Human Figure Control Using Tracked Blobs
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Context-Based Segmentation of Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Full-Body Gesture Database for Automatic Gesture Recognition
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
A Layered Deformable Model for Gait Analysis
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Tracking 3D Human Body using Particle Filter in Moving Monocular Camera
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Atomic Human Action Segmentation Using a Spatio-Temporal Probabilistic Framework
IIH-MSP '06 Proceedings of the 2006 International Conference on Intelligent Information Hiding and Multimedia
Semantic Modeling of Natural Scenes for Content-Based Image Retrieval
International Journal of Computer Vision
PCI'05 Proceedings of the 10th Panhellenic conference on Advances in Informatics
Semantic adaptation of sport videos with user-centred performance analysis
IEEE Transactions on Multimedia
Gesture-based interaction and communication: automated classification of hand gesture contours
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Spatiotemporal salient points for visual recognition of human actions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Motion segmentation by multistage affine classification
IEEE Transactions on Image Processing
Efficient, robust, and fast global motion estimation for video coding
IEEE Transactions on Image Processing
Automatic soccer video analysis and summarization
IEEE Transactions on Image Processing
Matching shapes with self-intersections: application to leaf classification
IEEE Transactions on Image Processing
MPEG-7 visual shape descriptors
IEEE Transactions on Circuits and Systems for Video Technology
Human Gait Recognition With Matrix Representation
IEEE Transactions on Circuits and Systems for Video Technology
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The automated analysis of activity in digital multimedia, and especially video, is gaining more and more importance due to the evolution of higher level video processing systems and the development of relevant applications such as surveillance and sports. This paper presents a novel algorithm for the recognition and classification of human activities, which employs motion and color characteristics in a complementary manner, so as to extract the most information from both sources, and overcome their individual limitations. The proposed method accumulates the flow estimates in a video, and extracts "regions of activity" by processing their higher order statistics. The shape of these activity areas can be used for the classification of the human activities and events taking place in a video and the subsequent extraction of higher-level semantics. Color segmentation of the active and static areas of each video frame is performed to complement this information. The color layers in the activity and background areas are compared using the earth mover's distance, in order to achieve accurate object segmentation. Thus, unlike much existing work on human activity analysis, the proposed approach is based on general color and motion processing methods, and not on specific models of the human body and its kinematics. The combined use of color and motion information increases the method robustness to illumination variations and measurement noise. Consequently, the proposed approach can lead to higherlevel information about human activities, but its applicability is not limited to specific human actions. We present experiments with various real video sequences, from sports and surveillance domains, to demonstrate the effectiveness of our approach.