Human Body Articulation for Action Recognition in Video Sequences
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Tracking HoG Descriptors for Gesture Recognition
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Video Surveillance Online Repository (ViSOR): an integrated framework
Multimedia Tools and Applications
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Recognizing different actions with a unique approach can be a difficult task. This paper proposes a novel holistic representation of actions that we called "action signature". This 1D trajectory is obtained by parsing the 2D image containing the orientations of the gradient calculated on the motion feature map called motion-history image. In this way, the trajectory is a sketch representation of how the object motion varies in time. A robust statistical framework based on mixtures of von Mises distributions and dynamic programming for sequence alignment are used to compare and classify actions/trajectories. The experimental results show a rather high accuracy in distinguishing quite complicated actions, such as drinking, jumping, or abandoning an object.