关键词: 行为识别, 梯度方向直方图, 完备局部二值模式, 径向基函数神经网络

Abstract: For the inadequate of Histogram of Oriented Gradients（HOG） feature for local gradient features of the human body in human action recognition, this paper presents a recognition algorithm of human action based on multi-features fusion using extended HOG feature and Complete Local Binary Pattern（CLBP） feature. The background subtraction algorithm is used to extract the complete human motion sequence in the video, and it extracts Extended HOG and CLBP feature of human body which are complementary. Then it fuses these two group features by K-L transform to get a new feature which has a higher discriminating power. At last, the paper uses radial basic function neural network to realize the action of multi class classification. The experimental results in the KTH and Weizmann behavior databases show the effectiveness of the proposed algorithm.

Key words: action recognition, Histogram of Oriented Gradients（HOG）, Complete Local Binary Pattern（CLBP）, radial basic function neural network