2024/09/05 by Federico Cunico, Cunico, Federico, Marco Cristani +1 · 1 citation
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face recognition and analysis #Gait Recognition and Analysis #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.2409.03879
openalex publication_date 2024/09/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In recent years, the development of deep learning approaches for the task of person re-identification led to impressive results. However, this comes with a limitation for industrial and practical real-world applications. Firstly, most of the existing works operate on closed-world scenarios, in which the people to re-identify (probes) are compared to a closed-set (gallery). Real-world scenarios often are open-set problems in which the gallery is not known a priori, but the number of open-set approaches in the literature is significantly lower. Secondly, challenges such as multi-camera setups, occlusions, real-time requirements, etc., further constrain the applicability of off-the-shelf methods. This work presents MICRO-TRACK, a Modular Industrial multi-Camera Reidentification and Open-set Tracking system that is real-time, scalable, and easy to integrate into existing industrial surveillance scenarios. Furthermore, we release a novel Re-ID and tracking dataset acquired in an industrial manufacturing facility, dubbed Facility-ReID, consisting of 18-minute videos captured by 8 surveillance cameras.