Prototype Learning Using Metric Learning Based Behavior Recognition

Abstract

Behavior recognition is an attractive direction in the computer vision domain. In this paper, we propose a novel behavior recognition method based on prototype learning using metric learning. Prototype learning algorithm can improve the classification performance of nearest-neighbor classifier, reduce the storage and computation requirements. And the metric learning algorithm is used to advance the performance of the prototype learning. In this paper, We use a kind of compound feature including local feature and motion feature to recognize human behaviors. The experimental results show the effectiveness of our method.