多检测器融合的深度相关滤波视频多目标跟踪算法  

Multi-detector Fusion-based Depth Correlation Filtering Video Multi-target Tracking Algorithm

作　　者：沈祥培 丁彦蕊 SHEN Xiang-pei;DING Yan-rui(Laboratory of Media Design and Software Technology,Jiangnan University,Wuxi,Jiangsu 214122,China;Schoolof Science,Jiangnan University,Wuxi,Jiangsu 214122,China)

机构地区：[1]江苏省媒体设计与软件技术重点实验室(江南大学),江苏无锡214122 [2]江南大学理学院,江苏无锡214122

出　　处：《计算机科学》2022年第8期184-190,共7页Computer Science

基　　金：国家自然科学基金(61772237);江苏省六大人才高峰基金会(XYDXX-030)。

摘　　要：在检测跟踪任务中,检测器存在误检和漏检目标的问题,导致依赖检测信息的视频多目标跟踪算法出现大量误跟和漏跟目标,这种漏跟和误跟会持续几十帧,降低了跟踪精度,为此提出了一种多检测器融合的深度相关滤波视频多目标跟踪算法。该算法融合多个检测器的信息,提出了一种新型融合机制,减少单个检测器的不足带来的漏检、误检数目,打破了单个检测器性能的局限性,使新生目标的获取更加可靠。此外,采用深度相关滤波算法ECO对目标进行逐个跟踪,并在原有ECO算法的基础上提出了一系列的改进方法,从而更贴合视频多目标跟踪任务,减少目标的漏跟数和身份标签跳变数。在MOT17数据集上进行实验,结果表明,与传统的视频多目标跟踪方法IOU17相比,所提算法的MOTA值从47.6提高至50.3,证明了所提方法在多目标跟踪研究上取得了很大的突破。In the detection and tracking task,the detector has mis-detected and missed targets.For video multi-target tracking algorithms that rely on detection information,there will be a large number of false tracking targets and missed targets.Such missed and false targets will last for dozens of frames,resulting in reduced tracking accuracy.Due to this reason,a multi-detector fusion deep correlation filter video multi-target tracking algorithm is proposed.It uses the information of multiple detectors and proposes a new fusion mechanism to reduce the number of missed detections and false detections caused by a single detector,and break the performance limitations of a single detector,which makes the acquisition of new targets more reliable.On the other hand,the deep correlation filter algorithm ECO is used to track the targets one by one,and a series of improvements are proposed on the basis of the original algorithm ECO,which is more suitable for the video multi-target tracking task,and reduces the number of missed targets and identity tag jumps.Finally,experiments are carried out on the MOT17 data set,compared with the traditional video multi-target tracking method IOU17,MOTA of the proposed algorithm improves from 47.6 to 50.3.It is proved that this method has made great improvement in the research of multi-target tracking.

关 键 词：多目标跟踪 多检测器融合 深度相关滤波 检测跟踪 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]