Research on Behavior Recognition Algorithm Based on Compact Representation of Low-level Feature

Abstract

Recently, the bag of visual words (BOVW) model based on spatial-temporal interest points (STIPs) is used more and more widely in the field of behavior recognition. however, the model ignores temporal order between frames and intra frame position information of interest points. In the paper, an algorithm is proposed to acquire the geometrical and temporal distribution of STIPs. Firstly, the STIPs mutual information(STIPsMI) algorithm based on Co-occurrence matrix is proposed to describe the spatial-temporal relationship of STIPs between different visual words. Then the descriptor is concatenated with the BOVW histogram as the final descriptor. The two authoritative human action datasets were used to test the algorithm in paper: the KTH and the UCF sports. Experimental results verify the robustness of the algorithm and better than BOVW model and other mainstream methods.