Chained-Tracker：端到端联合多目标检测和跟踪的成对注意回归结果链接
Chained-Tracker: Chaining Paired Attentive Regression Results for End-to-End Joint Multiple-Object Detection and Tracking
摘要：
现有的多目标跟踪（MOT）方法要么遵循逐检测跟踪的范式来分别进行目标检测、特征提取和数据关联，要么将这三个子任务中的两个子任务集成起来形成部分端到端的解决方案。超越这些次优框架，我们提出了一个名为Chained-Tracker（CTracker）的简单在线模型，它自然地将所有三个子任务集成到端到端解决方案中（据我们所知是第一个）。它将从重叠节点估计的成对边界框回归结果链接起来，其中每个节点覆盖两个相邻的帧。配对回归由对象注意（由检测模块引起）和身份注意（由ID验证模块确保）引起注意。两大新奇之处：链式结构和配对注意回归，使CTracker简单，快速和有效，在MOT 16和MOT 17挑战数据集上设置新的MOTA记录（分别为67.6和66.6），而不依赖于任何额外的训练数据。CTracker的源代码可以在github.com/pjl1995/CTracker上找到。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
