Track To Detect and Segment: An Online Multi-Object Tracker
Jialian Wu, Jiale Cao, Liangchen Song, Yu Wang, Ming Yang, Junsong Yuan
Abstract
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.
BibTeX
@inproceedings{cvpr2021_tracktodetectand,
title = {Track To Detect and Segment: An Online Multi-Object Tracker},
author = {Jialian Wu and Jiale Cao and Liangchen Song and Yu Wang and Ming Yang and Junsong Yuan},
booktitle = {CVPR 2021},
year = {2021}
}