TransTrack: Multiple Object Tracking with Transformer

In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple object tracking problems. TransTrack leverages the transformer architecture, which is an attention-based query-key mechanism. It applies object features from the previous frame as a query of the current frame an...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-05
Main Authors: Sun, Peize, Cao, Jinkun, Jiang, Yi, Zhang, Rufeng, Xie, Enze, Yuan, Zehuan, Wang, Changhu, Luo, Ping
Format: Article
Language:English
Subjects:
Algorithms
Feature extraction
Multiple target tracking
Object recognition
Queries
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this work, we propose TransTrack, a simple but efficient scheme to solve the multiple object tracking problems. TransTrack leverages the transformer architecture, which is an attention-based query-key mechanism. It applies object features from the previous frame as a query of the current frame and introduces a set of learned object queries to enable detecting new-coming objects. It builds up a novel joint-detection-and-tracking paradigm by accomplishing object detection and object association in a single shot, simplifying complicated multi-step settings in tracking-by-detection methods. On MOT17 and MOT20 benchmark, TransTrack achieves 74.5\% and 64.5\% MOTA, respectively, competitive to the state-of-the-art methods. We expect TransTrack to provide a novel perspective for multiple object tracking. The code is available at: \url{https://github.com/PeizeSun/TransTrack}.
ISSN:2331-8422