Human behavior recognition algorithm with space-time topological feature and sparse expression

Abstract

Behavior analysis based on vision is one of the important research topics in image processing,pattern recognition,etc,and it has wide application prospects on public security and military field.For the problems of a fixed camera such as lack of the single feature description,motion occlusions,holes and shadows,the paper proposed a behavior recognition algorithm which combines space-time topological feature with sparse expression.It used random projection to get a space-time topological feature of strong cohesion,high distinction and low dimension,which fused topology structure,geometric invariant and space-time Poisson information.The noise-adding sparse mechanism resolving problems by simulating human was combined to identify behaviors of human body in a close-range monitor scene.The experimental results show that the recognition rate of space-time topological feature is 12.79% higher than that of single one.The recognition rate of this proposed algorithm is only 6.15% down in a noisy scene,and that for multi-behavior reaches 87.78%.This algorithm has the properties of strong description for space-time feature,higher robustness against noise and high efficiency for behavior recognition.