Track to Detect and Segment: An Online Multi-Object Tracker

By Jialian Wu, Jiale Cao, Liangchen Song, Yu Wang, Ming Yang, Junsong Yuan. 2021

417 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. Alan L. Yuille
3. Chunhua Shen
4. Xinggang Wang
5. Xiaogang Wang
6. Ping Luo
7. Xiang Bai
8. Dacheng Tao

Citations in the corpus

• Occluded Video Instance Segmentation: A Benchmark (2021, methodology): “To dissect the OVIS dataset, we conduct a thorough evaluation of 9 state-of-the-art algorithms whose code is publicly available, including FEELVOS [51], IoUTracker+ [65], MaskTrack R-CNN [65], SipMask [7], STEm-Seg [2], STMask [31], TraDeS [59], CrossVIS [66], and QueryInst [14].”
• StrongSORT: Make DeepSORT Great Again (2022, methodology): “We train the detector on the CrowdHuman dataset [41] and MOT17 half training set for ablation following [66, 63, 45, 56, 61].”
• Occluded Video Instance Segmentation: Dataset and ICCV 2021 Challenge (2021, methodology): “We also evaluate nine open-source state-of-the-art methods (including FEELVOS [32], IoUTracker+ [39], MaskTrack R-CNN [39], SipMask [8], STEm-Seg [2], STMask [22], TraDeS [37], CrossVIS [40], and QueryInst [12]) on OVIS to analyze the OVIS dataset and serve as baselines for the OVIS challenge and fu”
• DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion (2021, methodology): “Motivated by these dataset properties, most recently developed multi-object tracking methods [23, 32, 33, 39] highly rely”