Abstract
The use of computers to understand video content can accurately and quickly label various videos. Behavior recognition technology can help users filter the video by screening the content. However, this calculation mode, which is only sensitive to the features in a pixel neighborhood, cannot effectively extract cross-frame long-range video features. In addition, the common long-range dependency capture methods are based on pixel pairs, which contain less semantic information and cannot accurately model dependencies. Based on this, this paper generates semantic units with rich semantic information in the form of neighborhood pixel aggregation and proposes a multi-semantic long-range dependency capture algorithm to solve this problem, which makes the established dependency relationship more accurate. At the same time, this paper proposes an early dependency transfer technology to speed up the reasoning speed of the multi-semantic long-range dependency capture algorithm. By embedding the proposed algorithm into the original convolutional neural network, and conducting sufficient performance tests and evaluations on different data sets, it is shown that the proposed algorithm outperforms other current algorithms in terms of recognition accuracy and achieves the optimal recognition effect, which can effectively enhance the long-range dependency capture ability and temporal modeling ability of the convolutional network, and improve the quality of video feature representation.