Using Discrete Cosine Transform Based Features for Human Action Recognition

Recognizing human action in complex video sequences has always been challenging for researchers due to articulated movements, occlusion, background clutter, and illumination variation. Human action recognition has wide range of applications in surveillance, human computer interaction, video indexing and video annotation. In this paper, a discrete cosine transform based features have been exploited for action recognition. First, motion history image is computed for a sequence of images and then blocked-based truncated discrete cosine transform is computed for motion history image. Finally, K-Nearest Neighbor (K-NN) classifier is used for classification. This technique exhibits promising results for KTH and Weizmann dataset. Moreover, the proposed model appears to be computationally efficient and immune to illumination variations; however, this model is prone to viewpoint variations

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