Multi-feature Fusion Action Recognition Based on Key Frames

Abstract

As an important technology in computer vision, human action recognition has a great commercial value, which has attracted extensive attention in both academia and industry. However, data redundancy and single feature were largely limited the accuracy of human action recognition. In this paper, adopting the Key frame extraction and multi-feature fusion techniques, a novel action recognition method was proposed, which can improve the recognition accuracy. The main works are as follows: 1) in order to solve the problem of data redundancy, a key frame extraction method based on node contribution weighting is proposed to extract video key frames; 2) different kinds of information flows are extracted from the obtained key frame sequences, and different convolutional neural networks are used to obtain corresponding classification results and merge, so as to better complement the information in different flows. Lastly, the experimental results show that our method improves the accuracy of action recognition.