Dense Trajectory Action Recognition Algorithm Based on Improved SURF

In order to improve the time-consuming and large error problem of camera motion estimation in dense trajectory feature extraction of video, a dense trajectory action recognition algorithm based on Improved Speeded-Up Robust Features (SURF) is proposed. The algorithm mainly performs dense sampling of video images, and then executes camera motion estimation. In the feature point detection stage, the Gaussian pyramid layer was constructed dynamically to improve the real-time and accuracy of feature point extraction. Based on the SURF algorithm, the brightness center algorithm is used to obtain direction of feature. Binary Robust Independent Elementary Feature (BRIEF) is used to generate feature descriptors to determine matching points and optimized images, then to conducts feature tracking and feature extraction on the images to classify features. The experimental results show that the algorithm performs better in terms of speed when removing camera motion, and improves the real-time performance of feature extraction and the accuracy of action recognition.