SimpleTrack: Rethinking and Improving the JDE Approach for Multi-Object Tracking

TL;DR

SimpleTrack introduces a novel association matrix and a bottom-up fusion method to enhance multi-object tracking, significantly improving accuracy and speed over existing JDE-based methods.

Contribution

The paper proposes the EG association matrix and a simple, effective tracker called SimpleTrack, advancing data association and re-identification in JDE-based multi-object tracking.

Findings

Achieved 61.6 HOTA and 76.3 IDF1 on MOT17.

Improved IDF1, HOTA, and IDsw metrics across five SOTA JDE methods.

Increased tracking speed by approximately 20%.

Abstract

Joint detection and embedding (JDE) based methods usually estimate bounding boxes and embedding features of objects with a single network in Multi-Object Tracking (MOT). In the tracking stage, JDE-based methods fuse the target motion information and appearance information by applying the same rule, which could fail when the target is briefly lost or blocked. To overcome this problem, we propose a new association matrix, the Embedding and Giou matrix, which combines embedding cosine distance and Giou distance of objects. To further improve the performance of data association, we develop a simple, effective tracker named SimpleTrack, which designs a bottom-up fusion method for Re-identity and proposes a new tracking strategy based on our EG matrix. The experimental results indicate that SimpleTrack has powerful data association capability, e.g., 61.6 HOTA and 76.3 IDF1 on MOT17. In addition, we apply the EG matrix to 5 different state-of-the-art JDE-based methods and achieve significant improvements in IDF1, HOTA and IDsw metrics, and increase the tracking speed of these methods by about 20%.