计算机科学 ›› 2022, Vol. 49 ›› Issue (8): 184-190.doi: 10.11896/jsjkx.210600004
沈祥培1, 丁彦蕊1,2
SHEN Xiang-pei1, DING Yan-rui1,2
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。
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