Abstract
Human Action Recognition plays a major role in building intelligent surveillance systems. To recognize actions, object tracking and feature extraction phases are crucial to detect the person and track its motion correspondingly. A good motion descriptor is to analyze the motion in each and every frame under different views. As a result, this paper addresses the two major challenges such as motion representation and action recognition. Thus a novel motion descriptor based on optical flow has been proposed to describe the activity of the persons though they are in various views. The new motion descriptor has been framed by fusing shape features and motion features together to increase the performance of the system. This has been fed as the input to the SVM classifier to recognize the human actions. Performance of this system has been tested over Weizmann and IXMAX multi-view data sets and results seem to be promising.
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Sivarathinabala, M., Abirami, S., Baskaran, R. (2015). View Invariant Human Action Recognition Using Improved Motion Descriptor. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 3. Smart Innovation, Systems and Technologies, vol 33. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2202-6_49
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DOI: https://doi.org/10.1007/978-81-322-2202-6_49
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