Symmetric Joint Detection and Re-Identification for UAV-Based Multiple Object Tracking
Symmetric Joint Detection and Re-Identification for UAV-Based Multiple Object Tracking
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DOI：
10.23919/ccc58697.2023.10240640
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发表时间：
2023-07
期刊：
影响因子：
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通讯作者：
Xi Li;Ruixiang Zhu;Xianguo Yu;Yirui Cong;Guoliang Liu
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文献类型：
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作者：
Xi Li;Ruixiang Zhu;Xianguo Yu;Yirui Cong;Guoliang Liu
随着多任务学习的发展，多目标跟踪（MOT）已以联合检测和重识别（JDR）模型为主导。 FairMOT 是这种范式下的里程碑式的工作。它认为无锚方法比基于锚的方法更适合构建 JDR 模型。然而，基于锚的探测器广泛应用于无人机（UAV）应用，因为它们往往能够在检测无人机视频中常见的小目标方面实现更高的性能。在本文中，我们针对与无人机视觉相关的 MOT 任务提出了一种基于锚的 JDR 模型。首先，为了平衡检测任务和重新识别（Re-ID）任务，提出了一种对称的两分支架构。其次，为了处理两个任务之间的对应问题，我们学习每个锚框的 Re-ID 特征。第三，为了缓解检测问题和Re-ID问题之间的冲突，我们分两步训练模型，首先训练检测分支，然后是Re-Id分支。我们的模型基于流行的基于锚的检测器 YOLOv7，在 Visdrone-MOT20 19 数据集上取得了出色的性能。实验结果表明，我们的模型可以有效地完成与无人机视觉相关的MOT任务。
With the development of multi-tasking learning, multi-object tracking (MOT) has been dominated by the model of Joint Detection and Re-Identification (JDR). FairMOT is a milestone work under such a paradigm. It contends that the anchor-free methods are more suitable than the anchor-based ones for building JDR models. However, the anchor-based detectors are widely used in unmanned aerial vehicle (UAV) applications since they tend to achieve higher performance for detecting small targets which are universal in drone videos. In this paper, we propose an anchor-based JDR model for the MOT task related to UAV vision. Firstly, to balance the detection task and Re-Identification (Re-ID) task, a symmetric two-branch architecture is proposed. Secondly, to deal with the correspondence problem between the two tasks, we learn Re-ID features for every anchor box. Thirdly, to alleviate the conflicts between the detection problem and the Re-ID problem, we train the model in two steps, where the detection branch is trained first, then is the Re- Id branch. Our model based on the popular anchor-based detector YOLOv7 has achieved great performance on the Visdrone- MOT20 19 dataset. The experimental results demonstrate that our model can validly complete the MOT task related to UAV visions.