Multi-object Tracking Using Hierarchical Data Association Based on Generalized Correlation Clustering Graphs
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摘要: 检测跟踪是近期多目标跟踪研究的热点方向之一.目前大部分方法都是基于相邻帧之间的双向匹配，对检测点进行数据融合.本文提出的方法是，给定一个滑动时间窗口，在窗口内对某个目标每帧出现的检测点进行一次性数据融合.我们把多目标跟踪看作图的分割问题，利用广义关联聚类（Generalized correlation clustering problem，GCCP）图优化文中提出的数据融合.吸取分层数据关联的思想，把多目标跟踪分成两个阶段.首先，在时间窗口内遵循检测点，利用广义关联聚类，得到自适应长度的轨迹片段，轨迹片段长度不受窗口宽度的限制.然后，基于轨迹片段进一步数据关联，得到目标的长轨迹.在公共数据集上的实验测试表明，本文方法能够有效地实现多目标跟踪，对于遮挡处理、身份转换处理以及轨迹的生成具有很好的鲁棒性，多目标跟踪准确率（Multiple object tracking accuracy，MOTA）超过当前水平.Abstract: Tracking by detection based on data association of detections is a main research direction in the field of multi-object tracking. The majority of current methods, such as bipartite matching, solve the data association problem between adjacent frames. We propose an approach to solve data association for one object whose all detections are in a sliding temporal window at a time. The task of multi-object tracking can be considered as a graph partitioning problem that takes the form of a generalized correlation clustering problem (GCCP). The multi-object tracking problem can be divided into two phases using hierarchical data association. Firstly, adaptive length tracklets can be obtained from all detections in a sliding temporal window by using the GCCP. The length of tracklets is not restricted to the window width. Secondly, we treat tracklets as detections, using the similar method described in first phase to acquire trajectories. Experiments show that the proposed method can handle occlusion and ID-switch effectively, which makes significant improvement in multi-object tracking on the public datasets. Our multiple object tracking accuracy (MOTA) is higher than that of the-state-of-the-art.
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表 1 自适应轨迹片段与传统轨迹片段比较
Table 1 The difference between traditional-tracklet and adaptive-tracklet
Methods MOTA(%) MOTP(%) IDS TC Time(s) Rcll(%) Prcsn(%) Traditional-tracklet 93.08 83.57 16 356 2.5 94.61 98.74 Adaptive-tracklet 95.11 83.04 4 77 2.34 96.31 98.88 
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表 2 跟踪结果
Table 2 The results of tracking
Sequence Methods MOTA(%) MOTP(%) Rcll(%) Prcsn(%) Longyin[13] 92.7 72.9 94.4 98.4 Andriyenko[15] 88.3 79.6 90 98.7 PETS-S2L1 Zamir[10] 90.3 69.02 96.45 93.64 Afshin[18] 90.4 63.12 -- -- Izadinia[12] 90.7 76 95.2 96.8 Ours 95.11 83.04 96.31 98.88 Longyin[13] 62.1 52.7 71.2 90.3 PETS-S2L2 Anton[26] 56.9 59.4 65.5 89.8 Ours 52.98 68.63 57.15 93.58 Longyin[13] 88.4 81.9 90.8 98.3 Andriyenko[15] 73.1 76.5 86.8 89.4 Parking lot Zamir[10] 90.43 74.1 85.3 98.2 Guang[8] 79.3 74.1 -- -- Izadinia[12] 88.9 77.5 96.5 93.6 Ours 91.37 74.8 96.61 95.24 Zamir[10] 75.59 71.93 -- -- Guang[8] 72.9 71.3 -- -- Town center McLaughlin[6] 74.15 72.41 83.27 90.4 Izadinia[12] 75.7 71.6 81.8 93.6 Ours 79.67 70.6 84.56 93.98
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