面向工业场景的多相机开放集行人重识别与跟踪系统
Multi-Camera Industrial Open-Set Person Re-Identification and Tracking
- 模块化设计支持实时、可扩展的多相机追踪
- 在8个摄像头18分钟视频上实现高精度开放集重识别
- 专为工厂监控场景定制，适合工业界快速部署
近年来，深度学习在行人重识别任务中取得了显著进展，但其在工业和实际应用中仍存在局限。首先，现有方法大多基于闭集场景，即待识别人员（探针）仅与已知画廊集对比；而真实场景常为开集问题，画廊集未知，但现有开集方法极少。其次，多相机设置、遮挡、实时性等挑战进一步限制了现成方法的应用。本文提出 MICRO-TRACK，一个模块化、实时、可扩展的工业级多相机重识别与开集追踪系统，易于集成至现有工业监控场景。此外，我们发布了在制造工厂采集的新数据集 Facility-ReID，包含8个摄像头拍摄的18分钟视频。
原文摘要 · Abstract (English)
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.
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