Eigenspace-based fall detection and activity recognition from motion templates and machine learning
Expert Systems with Applications: An International Journal
Gesture recognition using depth images
Proceedings of the 15th ACM on International conference on multimodal interaction
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Separating or segmenting complex activities into basic action primitives is important for event recognition and other applications. In this article, simple approaches are presented for appearance-based action recognition, as well as motion segmentation into its action primitives. Optical flow is computed and split into four channels based on four directions, namely, up, down, left, and right. Based on these four motion vectors, motion history and the corresponding energy templates are generated. These are used for action recognition. Moreover, to segment sequential activity, the temporal motion segmentation (TMS) method is proposed based on the concept of history templates. Based on the total pixel volumes on these templates and their related variations, various directions of the action primitives are segmented temporally. This segmentation method can assist an intelligent system or robot to understand activities and take decisions afterwards. It is a simple and real-time approach. Based on the presented experiments, this approach can be very useful in various application areas. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 91–99, 2009.