Multi-Camera Multi-Target Tracking (MCMT) is a computer vision technique that involves tracking multiple targets simultaneously across multiple cameras. MCMT in urban traffic visual analysis faces great challenges due to the complex and dynamic nature of urban traffic scenes, where multiple cameras with different views and perspectives are often used to cover a large city-scale area. Targets in urban traffic scenes often undergo occlusion, illumination changes, and perspective changes, making it difficult to associate targets across different cameras accurately. To overcome these challenges, we propose a novel systematic MCMT framework, called CityTrack. Specifically, we present a Location-Aware SCMT tracker which integrates various advanced techniques to improve its effectiveness in the MCMT task and propose a novel Box-Grained Matching (BGM) method for the ICA module to solve the aforementioned problems. We evaluated our approach on the public test set of the CityFlowV2 dataset and achieved an IDF1 of 84.91%, ranking 1st in the 2022 AI CITY CHALLENGE. Our experimental results demonstrate the effectiveness of our approach in overcoming the challenges posed by urban traffic scenes.

翻译：多摄像头多目标跟踪（MCMT）是一种跨多摄像头同步追踪多个目标的计算机视觉技术。在城市交通视觉分析中，由于城市场景的复杂性和动态性（通常需部署多个不同视角和视野的摄像头覆盖大面积城区），MCMT面临巨大挑战。目标在城市场景中常遭遇遮挡、光照变化及视角变换，导致跨摄像头精准关联目标困难重重。为解决上述问题，我们提出名为CityTrack的新型系统化MCMT框架。具体而言，我们构建了位置感知型单摄像头多目标跟踪器，该跟踪器集成多项先进技术以提升其在MCMT任务中的有效性；同时针对跨摄像头关联模块提出新型框粒度匹配（BGM）方法。在CityFlowV2数据集公开测试集上的评估显示，本方法IDF1指标达84.91%，荣膺2022年AI城市挑战赛榜首。实验结果充分验证了该方法应对城市交通场景挑战的有效性。