Action recognition by learnt class-specific overcomplete dictionaries

This paper presents a sparse signal representation based approach to address the problem of human action recognition in videos. For each action, a set of redundant basis (dictionary) is learnt by solving a sparse optimization problem. A dictionary is learnt using the image patches of its corresponding action, such that every patch vector is represented by some linear combination of a small number of basis vectors. By learning one dictionary per action, it is expected that each dictionary can efficiently represent one particular action. We show that such class-specific dictionaries - each representative of one action - provide a powerful means of action classification. Given a query sequence, the classifier seeks the dictionary that best approximates the query class. We have evaluated the proposed approach on the standard datasets. Experimental results demonstrate high accuracy and robustness against occlusion or viewpoint changes.