Abstract
Recent years, Dense Trajectory features has a crucial role in extracting implicit features for action recognition. The method encloses motion and appearance descriptors to specify characteristics of each trajectory. Moreover, combining gradient and optical flow field using tensor product has made a strong positive impact on the result as we introduced in our previous work. In this paper, a breakthrough concept of encoding a high dimensional unbound space using spherical coordinate is introduced and imposed to obtain sophisticated spherical tensor features. The experimental result shows that our propose features outperforms other conventional ones and the combination of all feature channels achieves the highest accuracy rate in our selfrecorded dataset.
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