SOM-based human action recognition using local feature descriptor CHOG3D

Human action recognition is applied in a wide field, such as video surveillance, intelligent interface, and intelligent robots. However, since various action classes, complex surrounding, interaction with objects, et al., it is still a complex problem to be solved. In this paper, we propose a method combining the Self-Organizing Map(SOM) and the classifier k-Nearest Neighbor algorithm (k-NN) to recognize human actions. We represent human actions in the form of local features using a compact descriptor, a histogram of oriented gradient in spatio-temporal 3D space(CHOG3D), which was proposed by us in the paper 1). Then we adopt SOM for feature training to extract key features of action information. With these key features, we adopt k-NN for action recognition. In our experiments, we test the optimal map size of SOM and the proper value k of k-NN for correct recognition. Our method is tested on KTH, Weizmann and UCF datasets, and results certify its efficiency.