论文标题
使用稀疏图跟踪器在线多对象跟踪中的检测恢复
Detection Recovery in Online Multi-Object Tracking with Sparse Graph Tracker
论文作者
论文摘要
在现有的联合检测和跟踪方法中，成对关系特征用于将先前的曲目与当前检测匹配。但是，这些功能可能没有足够的歧视性，无法从大量检测中识别目标。仅选择用于跟踪的高分检测可能会导致置信度得分较低的遗漏检测。因此，在在线环境中，这会导致无法恢复的曲目断开。在这方面，我们提出了稀疏的图形跟踪器（SGT），这是一种新颖的在线图形跟踪器，使用高阶关系特征，通过汇总相邻检测及其关系的特征，可以更具歧视性。中士将视频数据转换为图表，其中检测，它们的连接以及两个连接节点的关系特征分别由节点，边缘和边缘特征表示。强大的边缘功能使SGT可以通过Top-K评分检测到具有大K的跟踪候选者来跟踪目标。结果，即使是低得很低的检测，也可以跟踪遗漏的检测结果。 K值的鲁棒性通过广泛的实验显示。在MOT16/17/20和Hieve Challenge中，中士以实时推理速度优于最先进的跟踪器。特别是，MOT20和Hieve Challenge显示了MOTA的大幅度改进。代码可在https://github.com/hyunjs/sgt上找到。
In existing joint detection and tracking methods, pairwise relational features are used to match previous tracklets to current detections. However, the features may not be discriminative enough for a tracker to identify a target from a large number of detections. Selecting only high-scored detections for tracking may lead to missed detections whose confidence score is low. Consequently, in the online setting, this results in disconnections of tracklets which cannot be recovered. In this regard, we present Sparse Graph Tracker (SGT), a novel online graph tracker using higher-order relational features which are more discriminative by aggregating the features of neighboring detections and their relations. SGT converts video data into a graph where detections, their connections, and the relational features of two connected nodes are represented by nodes, edges, and edge features, respectively. The strong edge features allow SGT to track targets with tracking candidates selected by top-K scored detections with large K. As a result, even low-scored detections can be tracked, and the missed detections are also recovered. The robustness of K value is shown through the extensive experiments. In the MOT16/17/20 and HiEve Challenge, SGT outperforms the state-of-the-art trackers with real-time inference speed. Especially, a large improvement in MOTA is shown in the MOT20 and HiEve Challenge. Code is available at https://github.com/HYUNJS/SGT.
