  Most online multi-object trackers perform object detection stand-alone in a
neural net without any input from tracking. In this paper, we present a new
online joint detection and tracking model, TraDeS (TRAck to DEtect and
Segment), exploiting tracking clues to assist detection end-to-end. TraDeS
infers object tracking offset by a cost volume, which is used to propagate
previous object features for improving current object detection and
segmentation. Effectiveness and superiority of TraDeS are shown on 4 datasets,
including MOT (2D tracking), nuScenes (3D tracking), MOTS and Youtube-VIS
(instance segmentation tracking). Project page:
https://jialianwu.com/projects/TraDeS.html.
