Existing Multiple-Object Tracking (MOT) methods either follow the tracking-by-detection paradigm to conduct object detection, feature extraction and data association separately, or have two of the three subtasks integrated to form a partially end-to-end solution. Going beyond these sub-optimal frameworks, we propose a simple online model named Chained-Tracker (CTracker), which naturally integrates all the three subtasks into an end-to-end solution (the first as far as we know). It chains paired bounding boxes regression results estimated from overlapping nodes, of which each node covers two adjacent frames. The paired regression is made attentive by object-attention (brought by a detection module) and identity-attention (ensured by an ID verification module). The two major novelties: chained structure and paired attentive regression, make CTracker simple, fast and effective, setting new MOTA records on MOT16 and MOT17 challenge datasets (67.6 and 66.6, respectively), without relying on any extra training data. The source code of CTracker can be found at: github.com/pjl1995/CTracker.

提出了一种名为Chained-Tracker（CTracker）的简单在线模型，它自然地将物体检测、特征提取和数据关联三个子任务集成到了一个端到端的解决方案中，并且采用了链式结构和成对关注回归技术，将MOTA记录提高到了67.6和66.6，在MOT16和MOT17挑战数据集上实现了简单、快速、有效的多目标跟踪。

Chained-Tracker：端到端多目标检测和跟踪的配对注意回归结果链式化