Real-time Video-based Person Re-identification Surveillance with Light-weight Deep Convolutional Networks

Today's person re-ID system mostly focuses on accuracy and ignores efficiency. But in most real-world surveillance systems, efficiency is often considered the most important focus of research and development. Therefore, for a person re-ID system, the ability to perform real-time identification is the most important consideration. In this study, we implemented a real-time multiple camera video-based person re-ID system using the NVIDIA Jetson TX2 platform. This system can be used in a field that requires high privacy and immediate monitoring. This system uses YOLOv3-tiny based light-weight strategies and person re-ID technology, thus reducing 46% of computation, cutting down 39.9% of model size, and accelerating 21% of computing speed. The system also effectively upgrades the pedestrian detection accuracy. In addition, the proposed person re-ID example mining and training method improves the model's performance and enhances the robustness of cross-domain data. Our system also supports the pipeline formed by connecting multiple edge computing devices in series. The system can operate at a speed up to 18 fps at 1920x1080 surveillance video stream.