Estimating Human Body Configurations Using Shape Context Matching
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
The Representation and Recognition of Human Movement Using Temporal Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Real-time Human Motion Analysis by Image Skeletonization
WACV '98 Proceedings of the 4th IEEE Workshop on Applications of Computer Vision (WACV'98)
Shape Matching and Object Recognition Using Shape Contexts
Shape Matching and Object Recognition Using Shape Contexts
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
View-Invariant Human Activity Recognition Based on Shape and Motion Features
ISMSE '04 Proceedings of the IEEE Sixth International Symposium on Multimedia Software Engineering
3D Shape Context Based Gesture Analysis Integrated with Tracking using Omni Video Array
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Continuous Human Action Segmentation and Recognition Using a Spatio-Temporal Probabilistic Framework
ISM '06 Proceedings of the Eighth IEEE International Symposium on Multimedia
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cross-View Action Recognition from Temporal Self-similarities
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Shape retrieval and recognition based on fuzzy histogram
Journal of Visual Communication and Image Representation
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In this paper, a four-dimensional spatiotemporal shape context descriptor is introduced and used for human activity recognition in video. The spatiotemporal shape context is computed on silhouette points by binning the magnitude and direction of motion at every point with respect to given vertex, in addition to the binning of radial displacement and angular offset associated with the standard 2D shape context. Human activity recognition at each video frame is performed by matching the spatiotemporal shape context to a library of known activities via k-nearest neighbor classification. Activity recognition in a video sequence is based on majority classification of the video frame results. Experiments on the Weizmann set of ten activities indicate that the proposed shape context achieves better recognition of activities than the original 2D shape context, with overall recognition rates of 90% obtained for individual frames and 97.9% for video sequences.