Research on Human Behavior Recognition Based on Video Key Frame

Abstract

In order to solve the problems of low recognition accuracy and high computational complexity caused by redundant video data in the existing behavior recognition process, a human behavior recognition method based on video key frame (S3DCCA) is proposed. First of all, structural similarity (SSIM) algorithm is used to calculate the difference of luminance, contrast and structure between the two frames, and the result is multiplied to attain SSIM value, then select the local and global key frame in the human motion video frame sequence according to the SSIM value. Finally, the selected key frame are used as the input of three-dimensional convolutional neural networks and attention mechanism Channel attention (3DCCA) model to recognize human behavior. Experimental results on UCF101 and HMDB51 datasets show that the proposed method has high recognition rate.