Human action recognition using Action Trait Code

Recognizing actions having similar movements is a challenging problem. Human action understanding task is divided into two issues in this paper. One is a classical action recognition task where we employ a probabilistic model to learn and recognize human actions. The other is action categorization task where we classify actions based on quantized human movement. An approach called Action Trait Code (ATC) for human action classification is proposed to represent an action with a set of velocity types derived by the averages velocity of each body part. An effective graph model based on ATC classification is employed for learning and recognizing human actions. To examine recognition accuracy, we evaluate our approach on Cornell Kinect Activity Database and compare with a hierarchical maximum entropy Markov model (MEMM). Besides, the results on self-collected action database demonstrate that the proposed approach not only successfully achieves high recognition accuracy but also performs in real-time.