CityTrack: Improving City-Scale Multi-Camera Multi-Target Tracking by
  Location-Aware Tracking and Box-Grained Matching
Jincheng Lu Xipeng Yang Jin Ye Yifu Zhang Zhikang Zou Wei Zhang Xiao Tan
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.