Improvement And Application Of A Specific Multi-object Tracking Algorithm Under Cross-domain Environments

Cross-domain multi-object tracking is a challenging and difficult direction in the field of computer vision,and this direction can effectively expand the monitoring range and improve security compared with single-camera tracking with limited monitoring area.However,with the increase in the number of surveillance cameras,the amount of data in surveillance video has also risen sharply.The traditional method based on manual monitoring has been difficult to meet the security needs,and intelligent methods and technologies are urgently needed to address this issue.This article takes the specific target of not wearing a helmet on the construction site as a starting point to conduct cross domain tracking of this specific target,to achieve the goal of safety warning.The main research work of this article is as follows:Firstly,Research on the implementation of cross domain specific multi object tracking: The task is divided into object detection,multi object tracking,and cross domain target matching.Finally the author will verify the feasibility and robustness of this scheme through experiments.Secondly,Ameliorate the object detection algorithm: Aiming at issues such as small targets and similar appearances in video surveillance,Dense Net core block Denseblock is used to improve the backbone network of YOLOv5 detection network,and three strategies are designed to compare and select the most effective strategy.Experimental verification shows that the optimized YOLOv5 algorithm can not only avoid over fitting,but also improve the detection accuracy for small targets.Thirdly.Ameliorated multi object tracking algorithm: Deep SORT algorithm is optimized for tracking failures caused by mutual occlusion between targets and sudden changes in target motion status.The detection frame and the deterministic tracking frame are matched with Io U before cascading matching,followed by the low-score detection frame and the tracking box that are not successfully matched by GIo U for secondary matching,and the improved detection algorithm is used as the detector of the optimized tracking algorithm.Experiments have verified that the improved algorithm not only successfully tracks targets that suddenly change due to motion state,but also improves the tracking accuracy of small targets.Finally the operation efficiency of the algorithm will be improved.The ameliorated algorithm is combined with the target recognition algorithm to achieve specific multi target tracking in cross domain environments.The algorithm is applied to specific multi target tracking systems in cross domain environments,effectively implementing safety warning has reduced the frequency of unexpected accidents and provided practical guarantees for safety production.