The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Human motion analysis: a review
Computer Vision and Image Understanding
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Lucas/Kanade meets Horn/Schunck: combining local and global optic flow methods
International Journal of Computer Vision
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Statistical Analysis of Dynamic Actions
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Two-frame motion estimation based on polynomial expansion
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
Human action recognition using distribution of oriented rectangular patches
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
User recognition based on continuous monitoring and tracking
Proceedings of the 6th international conference on Human-robot interaction
Human action recognition by extracting features from negative space
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
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This paper addresses the human action recognition task from optical flow. We develop a non-parametric motion model using only the image region surrounding the actor making the action. For every two consecutive frames, a local motion descriptor is calculated from the optical flow orientation histograms collected from overlapping regions inside the bounding box of the actor. An action descriptor is built by weighting and aggregating the estimated histograms along the temporal axis. We obtain a promising trade-off between complexity and performance compared with state-of-the-art approaches. Experimental results show that the proposed method equals or improves on the performance of state-of-the-art approaches using these databases.