The growing need for video surveillance in public spaces has created a demand
for systems that can track individuals across multiple cameras feeds in
real-time. While existing tracking systems have achieved impressive performance
using deep learning models, they often rely on pre-exist
提出了一种基于深度学习检测和追踪的重新检测和链接模块，并结合粗到细轴向注意网络优化了视频行人重识别，可以显著降低计算成本并在 MARS 数据集上实现排名 1 的最高表现，能够解决由于不完美的检测和跟踪结果导致的空间和时间错配问题，同时发现了数据集中的噪声和评估协议错误，为是次领域研究提供了可靠的数据和基础工具。