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Learning sequence-to-sequence affinity metric for near-online multi-object tracking
Knowledge and Information Systems ( IF 2.7 ) Pub Date : 2020-07-30 , DOI: 10.1007/s10115-020-01488-7
Weijiang Feng , Long Lan , Xiang Zhang , Zhigang Luo

In this paper, we propose a sequence-to-sequence affinity metric for the data association of near-online multi-object tracking. The proposed metric learns the affinity between track sequence consisting of the already associated detections and hypothesis sequence consisting of detections in the near future. With the potential hypothesis sequences, we leverage the idea that if a track sequence has a high affinity for a hypothesis sequence, and the hypothesis sequence also shares a close affinity for a current detection, then the affinity between the track sequence and the detection is high. By using the short hypothesis sequence as a “bridge”, the proposed sequence-to-sequence affinity metric enhances the conventional track sequence to detection affinity metric and improves its robustness to object occlusion and missing. Besides, in order to eliminate the negative effects of false alarms, we propose a false alarm model using both appearance and scale features of detection. The robustness of the proposed affinity metric allows us to use a simple greedy data association algorithm. Experimental results on the challenging MOT16 and MOT17 benchmarks demonstrate the effectiveness of our method.



中文翻译:

学习序列对序列的亲和度度量,用于近在线多对象跟踪

在本文中,我们为近在线多目标跟踪的数据关联提出了序列间亲和力度量。所提出的度量了解在不久的将来由已经关联的检测组成的跟踪序列与由检测组成的假设序列之间的亲和力。对于潜在的假设序列,我们利用以下想法:如果跟踪序列与假设序列具有较高的亲和力,并且假设序列与当前检测也具有紧密的亲和力,则跟踪序列与检测之间的亲和力较高。通过使用短假设序列作为“桥梁”,提出的序列到序列亲和力度量增强了常规跟踪序列的检测亲和力度量,并提高了其对对象遮挡和丢失的鲁棒性。除了,为了消除虚假警报的负面影响,我们提出了一种利用外观和尺度特征进行检测的虚假警报模型。所提出的亲和度度量的鲁棒性允许我们使用简单的贪婪数据关联算法。在具有挑战性的MOT16和MOT17基准测试上的实验结果证明了我们方法的有效性。

更新日期:2020-10-04
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