Human Behavior Recognition Based on Cuckoo Search Algorithm Optimizing Features and Classifier Parameters

Behavior features and classifier parameters directly influence the accuracy and efficiency of behavior recognition, in order to obtain ideal results for human action recognition, a recognition model is proposed by using cuckoo search algorithm optimizing behavior features and classifier parameters. Firstly, human behavior features are extracted and features are normalized, and secondly relevance vector machine is taken as the human behavior recognition classifier which the range of parameters are determined, finally, cuckoo search algorithm is used to optimize features and classifier parameters of human behavior to establish recognition model of human behavior. Simulation experimental results show that the proposed model can quickly and effectively find human behavior features and classifier parameters, can improve the recognition correct rate of human behavior, and the recognition efficiency is better than the contrast models.