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Multi-Camera Multi-Target Tracking (MCMT) is a computer vision technique thatinvolves tracking multiple targets simultaneously across multiple cameras. MCMTin urban traffic visual analysis faces great challenges due to the complex anddynamic nature of urban traffic scenes, where multiple cameras with differentviews and perspectives are often used to cover a large city-scale area. Targetsin urban traffic scenes often undergo occlusion, illumination changes, andperspective changes, making it difficult to associate targets across differentcameras accurately. To overcome these challenges, we propose a novel systematicMCMT framework, called CityTrack. Specifically, we present a Location-AwareSCMT tracker which integrates various advanced techniques to improve itseffectiveness in the MCMT task and propose a novel Box-Grained Matching (BGM)method for the ICA module to solve the aforementioned problems. We evaluatedour approach on the public test set of the CityFlowV2 dataset and achieved anIDF1 of 84.91%, ranking 1st in the 2022 AI CITY CHALLENGE. Our experimentalresults demonstrate the effectiveness of our approach in overcoming thechallenges posed by urban traffic scenes.

CityTrack: Improving City-Scale Multi-Camera Multi-Target Tracking by Location-Aware Tracking and Box-Grained Matching