In this work, we have presented an end-to-end multi-person multi-camera tracking (MPMCT) surveillance system and implemented it on edge analytics platform for real-time performance. The proposed MPMCT framework is both privacy-aware and scalable supporting a processing pipeline on the edge consisting of person detection, tracking and robust person re-identification. A realistic and large dataset has been created to train and evaluate the surveillance system that has been employed to track people inside the institute campus throughout the entire day. Appropriate deep-learning algorithms and real-time implementation strategies have been employed to realize the MPMCT system on NVIDIA Jetson TX2 embedded platform with real-time performance. The proposed system has an IDF1 score of 90.97 on our dataset and outperforms the current state-of-the-art real-time algorithms. The performance up to 30 FPS is achieved for the person detection algorithm, whereas an average latency of 90 ms is achieved for the re-identification algorithm.

在这项工作中，我们介绍了一个端到端的多人多摄像机跟踪（MPMCT）监视系统，并将其在边缘分析平台上实现了实时性能。所提出的MPMCT框架既具有隐私意识，又具有可扩展性，可支持边缘处理流程，包括人员检测，跟踪和可靠的人员重新识别。已经创建了一个现实的大型数据集来训练和评估监视系统，该系统已被用于全天跟踪研究所校园内的人员。已采用适当的深度学习算法和实时实现策略在具有实时性能的NVIDIA Jetson TX2嵌入式平台上实现MPMCT系统。提议的系统的IDF1得分为90。在我们的数据集上达到97，并且优于当前最新的实时算法。人物检测算法的性能高达30 FPS，而重新识别算法的平均延迟为90 ms。