Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Learning key lines for multi-object tracking

Li, Yi-Fan, Ji, Hong-Bing, Chen, Xi, Yang, Yong-Liang and Lai, Yu-Kun ORCID: 2024. Learning key lines for multi-object tracking. Computer Vision and Image Understanding 241 , 103973. 10.1016/j.cviu.2024.103973
Item availability restricted.

[thumbnail of LineTrackCVIU.pdf] PDF - Accepted Post-Print Version
Restricted to Repository staff only until 20 February 2025 due to copyright restrictions.

Download (9MB)


Most online multi-object tracking methods utilize bounding boxes and center points inherited from detectors as the base models to represent targets. Limited performance is obtained with these base models alone for tracking. Complex networks are generally applied on top to extract high-level discriminative features such as appearance embeddings and motion predictions for data association. However, the weakness in the feature representation of bounding boxes and center points degrades the tracking performance. In this paper, we propose a novel base model that represents targets with key lines for tracking, which can provide discriminative features and accurate target affinity measurements. Besides, we use the proposed key lines to select low-scored detections and unmatched tracks to recover missed targets and enhance identity consistency. Based on this, we apply the proposed line-based modeling strategy to existing trackers and propose a line-based Cascade Tracking algorithm to associate targets in three stages, and very competitive results are achieved on MOTChallenge benchmarks. Extensive experiments with improved performances demonstrate the effectiveness and generalization of key lines in providing discriminative features and enhancing tracking performance.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Additional Information: License information from Publisher: LICENSE 1: URL:, Start Date: 2026-02-22
Publisher: Elsevier
ISSN: 1077-3142
Date of First Compliant Deposit: 26 February 2024
Date of Acceptance: 14 February 2024
Last Modified: 26 Feb 2024 15:46

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics