Hugging Face's logo
Papers
arxiv:2103.08808

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

Published on Mar 16, 2021
Authors:
,
,
,
,
,

Abstract

TraDeS integrates tracking offsets via cost volumes to propagate features and jointly improve online detection and segmentation across 2D, 3D, and video instance tracking tasks.

Generated by thinkingmachines/Inkling-Small

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.

View arXiv page View PDF Add to collection

Community

Sign up or log in to comment

Get this paper in your agent:

hf papers read 2103.08808
Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash

Models citing this paper 0

No model linking this paper

Cite arxiv.org/abs/2103.08808 in a model README.md to link it from this page.

Datasets citing this paper 1

Spaces citing this paper 0

No Space linking this paper

Cite arxiv.org/abs/2103.08808 in a Space README.md to link it from this page.

Collections including this paper 0

No Collection including this paper

Add this paper to a collection to link it from this page.