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Human action recognition using key poses and atomic motions

Human action recognition is a fundamental skill for personal assistive robotics to observe and automatically react to human's daily activities. Generally, one human activity can be intuitively considered as a sequence of key poses and atomic motions. Thus, a human action recognition algorithm based on key poses and atomic motions is proposed in this paper. Firstly, the normalized relative orientations of human joints are computed as the skeletal features. Secondly, the skeletal feature sequences are segmented into static segments and dynamic segments based on the kinetic energy. Then, the codebook of key poses is constructed from the static segments using clustering algorithms, and the codebook of atomic motions is constructed from the associated dynamic segments with any two key poses. Lastly, the activity patterns are constructed and the Naïve Bayes Nearest Neighbor algorithm is utilized to classify human activities based on the training and testing activity pattern matching. The Cornell CAD-60 dataset is used to test the proposed algorithm. The experimental results show that the proposed algorithm can obtain a better performance than the state-of-the-art algorithms.