Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
International Journal of Computer Vision
Statistical Analysis of Dynamic Actions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking People by Learning Their Appearance
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
On the Spatial Statistics of Optical Flow
International Journal of Computer Vision
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Hierarchical properties of multi-resolution optical flow computation
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
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This paper presents a novel descriptor for activity classification. The intuition behind the descriptor is "learning" statistics of optical flow histograms (as opposed to learning "raw" histograms). Towards this end, an activity descriptor capturing histogram statistics is constructed. Further, a technique to make the feature descriptor scale-invariant and parts-based is proposed. The approach is validated on a dataset collected from a camera network. The data presents a challenging real world scenario (variable frame rate recording, significant depth disparity, and severe clutter), where biking, skateboarding, and walking are activities to be classified. Experimental results point to the promise of the proposed descriptor in comparison to state of the art.