# State-aware Re-identification Feature for Multi-target Multi-camera Tracking

arXiv: https://arxiv.org/abs/1906.01357 · HTML: https://mlsanity.com/paper/1906.01357/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2019.

## Built on by (most cited first, 7 of 7)

- [Adaptive Affinity for Associations in Multi-Target Multi-Camera Tracking](https://mlsanity.com/paper/2112.07664/) (2021)
- [GMT: Effective Global Framework for Multi-Camera Multi-Target Tracking](https://mlsanity.com/paper/2407.01007/) (2024)
- [A Robust Deep Networks based Multi-Object MultiCamera Tracking System for City Scale Traffic](https://mlsanity.com/paper/2505.00534/) (2025)
- [Tracking Passengers and Baggage Items using Multiple Overhead Cameras at Security Checkpoints](https://mlsanity.com/paper/2301.00190/) (2022)
- [Towards Simple and Accurate Human Pose Estimation with Stair Network](https://mlsanity.com/paper/2202.09115/) (2022)
- [Towards High Performance One-Stage Human Pose Estimation](https://mlsanity.com/paper/2301.04842/) (2023)
- [ARGUSTRACK: A Multi-View Annotation System for Multi-Object Tracking](https://mlsanity.com/paper/2606.20687/) (2026)

## Abstract (arXiv)

Multi-target Multi-camera Tracking (MTMCT) aims to extract the trajectories from videos captured by a set of cameras. Recently, the tracking performance of MTMCT is significantly enhanced with the employment of re-identification (Re-ID) model. However, the appearance feature usually becomes unreliable due to the occlusion and orientation variance of the targets. Directly applying Re-ID model in MTMCT will encounter the problem of identity switches (IDS) and tracklet fragment caused by occlusion. To solve these problems, we propose a novel tracking framework in this paper. In this framework, the occlusion status and orientation information are utilized in Re-ID model with human pose information considered. In addition, the tracklet association using the proposed fused tracking feature is adopted to handle the fragment problem. The proposed tracker achieves 81.3% IDF1 on the multiple-camera hard sequence, which outperforms all other reference methods by a large margin.