Multi-Camera Industrial Open-Set Person Re-Identification and Tracking
<|reference_start|>Multi-Camera Industrial Open-Set Person Re-Identification and Tracking: 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 Re_identification 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.<|reference_end|>
@article{cunico2024multi-camera,
    title={Multi-Camera Industrial Open-Set Person Re-Identification and Tracking},
    author={Federico Cunico, Marco Cristani},
    journal={arXiv preprint arXiv:2409.03879},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03879},
    primaryClass={cs.CV}
}