Human Behavior Recognition Algorithm Based on HOG Feature and SVM Classifier

Human behavior analysis is a hot research in the field of computer vision. It has broad application prospects in the fields of intelligent monitoring, human-computer interaction, motion analysis and virtual reality. In order to improve the accuracy of human behavior recognition, a human behavior recognition method based on HOG feature and SVM classifier is proposed. First, the HOG features of the training set and the test set are extracted. Then, the multi-class problem is transformed into multiple dual-class problems, and multiple SVM classifiers are trained by using the HOG features. Finally, the trained classifiers are employed to recognize the human walking and waving behavior. Experimental results show that the recognition rate of walking and waving is 87.5% for the UIUC database. The human behavior recognition method proposed in this paper can effectively improve the recognition rate.