Rethinking the competition between detection and ReID in Multi-Object Tracking

TL;DR

This paper introduces a novel reciprocal network and scale-aware attention mechanism to improve the cooperation between detection and ReID tasks in one-shot multi-object tracking, achieving state-of-the-art results efficiently.

Contribution

It proposes a reciprocal network with self-relation and cross-relation design, and a scale-aware attention network to enhance task-dependent representations and association capability in one-shot MOT.

Findings

Achieves state-of-the-art performance on MOT16, MOT17, and MOT20 datasets.

Runs at 16.4 FPS on a single GPU, with a lightweight version at 34.6 FPS.

Effectively alleviates task competition and improves detection-ReID cooperation.

Abstract

Due to balanced accuracy and speed, one-shot models which jointly learn detection and identification embeddings, have drawn great attention in multi-object tracking (MOT). However, the inherent differences and relations between detection and re-identification (ReID) are unconsciously overlooked because of treating them as two isolated tasks in the one-shot tracking paradigm. This leads to inferior performance compared with existing two-stage methods. In this paper, we first dissect the reasoning process for these two tasks, which reveals that the competition between them inevitably would destroy task-dependent representations learning. To tackle this problem, we propose a novel reciprocal network (REN) with a self-relation and cross-relation design so that to impel each branch to better learn task-dependent representations. The proposed model aims to alleviate the deleterious tasks competition, meanwhile improve the cooperation between detection and ReID. Furthermore, we introduce a scale-aware attention network (SAAN) that prevents semantic level misalignment to improve the association capability of ID embeddings. By integrating the two delicately designed networks into a one-shot online MOT system, we construct a strong MOT tracker, namely CSTrack. Our tracker achieves the state-of-the-art performance on MOT16, MOT17 and MOT20 datasets, without other bells and whistles. Moreover, CSTrack is efficient and runs at 16.4 FPS on a single modern GPU, and its lightweight version even runs at 34.6 FPS. The complete code has been released at https://github.com/JudasDie/SOTS.