基于改进Tracktor的行人多目标跟踪算法  

Improved Tracktor-Based Pedestrian Multi-Objective Tracking Algorithm

作　　者：谌海云[1] 黄忠义 王海川 余鸿皓 SHEN Haiyun;HUANG Zhongyi;WANG Haichuan;YU Honghao(School of Electrical Information,Southwest Petroleum University,Chengdu 610500,China)

机构地区：[1]西南石油大学电气信息学院,成都610500

出　　处：《计算机工程与应用》2024年第8期242-249,共8页Computer Engineering and Applications

基　　金：南充市校科技战略合作项目(SXHZ053,SXJBGS002)。

摘　　要：在多目标视频跟踪中,针对受交互遮挡等影响导致检测偏差从而致使目标身份丢失的问题,提出一种基于改进Tracktor的行人多目标跟踪算法DUTracktor。在检测框回归中设计一个动态更新模块,利用孪生网络对建议框进一步检测定位;利用时序信息增强模块更新当前帧更适合的模板,建立全局上下文关系;并通过像素相关进行特征融合,从而增强目标边缘信息和尺度信息;利用相机运动补偿和融合相似矩阵构建二级关联跟踪机制,建立检测框和轨迹更强大的关联性,提高目标跟踪的鲁棒性。在公开的MOT16数据集上进行实验测试,并与当前主流算法相比,该算法跟踪精度表现较优,具有良好的鲁棒性,FPS稳定在24帧。In multi-target video tracking,for the problem of detection bias caused by interaction occlusion and other influ-ences,thus resulting in target identity loss,an improved Tracktor-based pedestrian multi-target tracking algorithm is proposed.Firstly,a dynamic update module is designed in the detection frame regression to further detect and locate the proposed frame by using twin networks.Then,the temporal information enhancement module is used to update a more suitable template for current frame and establish global contextual relationships.And feature fusion is performed through pixel correlation,thus enhancing target edge information and scale information.Finally,camera motion compensation and fusion similarity matrix are adopted to construct a secondary correlation tracking mechanism to establish stronger correlation between detection frame and trajectory,and improve the robustness of target tracking.Experimental tests are conducted on public available MOT16 dataset,in comparasion with current mainstream algorithms,the tracking accuracy perfor-mance of the proposed algorithm is better,which has good robustness with a stable FPS of 24 frames.

关 键 词：计算机视觉 多目标跟踪 Tracktor 孪生网络 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]