Research On Temporal Behavior Detection Method Based On Weakly Supervised Learning And System Implementatio

Temporal action detection of video is a challenging research direction of computer vision,and has a good application prospect in security,new retail and social media.Compared with full supervision,weak supervision has less labeling burden and cost.However,since the training data of Weakly-supervised temporal action detection only has video-level action category label,the following two problems still exist in the research in this field :(1)action and background confusion;(2)The actions identified are incomplete.In order to solve the above problems,this paper proposes two weakly-supervised temporal action detection algorithms based on knowledge distillation.In addition,this paper implements a weakly-supervised temporal action detection system.The research content of this paper is as follows:(1)A weakly-supervised temporal action detection method based on self-distillation fusion is proposed to alleviate the confusion between actions and backgrounds.Based on the principle of self-distillation,the low-level network provides segment-level supervision information for the high-level network,and then fuses its output classification results,so that the results can have more accurate semantic information and distinguish background and action to the greatest extent.This method utilizes the output of the low-level network to supervise the learning of the high-level network,so that the feature semantic information can be more fully utilized.Experiments show that the proposed self-distillation fusion method can effectively alleviate the problem of indistinguishable actions and backgrounds in the temporal action detection.(2)A weakly-supervised temporal action detection framework based on motion distillation is proposed to make the detected actions more complete in time sequence.The method uses a motion feature enhancement module to enhance the optical flow information,and then the teacher branch teaches the rich motion information to the student branch,so that the student branch can pays attention to the insignificant motion part that the teacher branch ignores,which can make the action of detection more complete.The experiments show that the proposed motion feature enhancement module can more fully mine and utilize motion information,and prove that the motion distillation method can effectively improve the integrity of the detected action.(3)Based on the method proposed in this paper,a weakly-supervised temporal action detection system is designed and implemented.The system can intuitively see the detection effect of a single video,that is,the categories of actions in the video as well as the start time and end time of the actions will be displayed in the front interface.