  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.
