Vector Quantization based Pairwise Joint Distance Maps (VQ-PJDM) for 3D Action Recognition

Abstract

This paper presents an approach for 3D action recognition using vector quantization with pairwise joint distance maps. We name this approach as VQ-PJDM. The main problem for 3D action recognition using skeleton data is that dealing with the variable length of action sequences. We solve this problem by approximation of each action sequence as a codebook, which is the output of Vector Quantization (VQ) method. The codebook size is fixed for any length of the action sequence. After all actions in the data set are approximated by VQ method, the Pairwise Distance Joint Distance Maps(PJDM) are calculated from approximated actions. The voting classifier is employed for action classification. The empirical results on the UT Kinect dataset prove that the proposed method gives better results than that of state of the art.