Auxiliary Labeling System Based On Target Tracking

With the continuous development of computer vision technology,video products have gradually become intelligent.Artificial intelligence algorithms usually require a large number of sample data for training,so a large number of open source data sets appear.However,when applying artificial intelligence algorithms to actual projects,because the problems to be processed have different characteristics,it is not enough to use public data sets,and private data sets also need to be more targeted training.Constructing private data sets using only manual annotation requires a lot of manpower and time costs.However,some existing systems that use crowdsourcing or sampling for labeling have the disadvantages of complex labeling process and low accuracy.In order to solve the above problems,this thesis designs and implements an auxiliary tagging system based on target detection and tracking,which helps manual semi-automatic tagging of videos,reduces the workload and difficulty of manual tagging,and greatly improves the efficiency of tagging.First,this thesis studies the target detection and target tracking algorithms.The three target detection algorithms of Faster R-CNN,YOLOv3 and SSD are combined with the Kalman filter tracking algorithm respectively to generate the target detection frame and target tracking frame in each frame of video,and use the Hungarian algorithm for target assignment to generate the frame The label information corresponding to the target.The user can select a suitable algorithm for initial labeling according to the video to improve the accuracy of labeling.Secondly,extract key frames and perform manual verification.In this thesis,the lens boundary detection algorithm and the custom error-prone frame selection algorithm are used to extract key frames during the calculation of the labeling algorithm,and define manual verification rules.Implement corrections to the entire video.Finally,build an auxiliary annotation system,which implements the auxiliary annotation function for the videos uploaded by users.The system realizes the initial annotation of the video by implementing the auxiliary annotation algorithm,and then modifies the information such as the position and size of the target frame in the key frame through manual verification.After the annotation is completed,the user can download the annotation file in the required format.Tests show that the system can play a good auxiliary role in the video labeling process,greatly shorten the labeling time,and has high practicality.