  Multi-camera vehicle tracking is one of the most complicated tasks in
Computer Vision as it involves distinct tasks including Vehicle Detection,
Tracking, and Re-identification. Despite the challenges, multi-camera vehicle
tracking has immense potential in transportation applications including speed,
volume, origin-destination (O-D), and routing data generation. Several recent
works have addressed the multi-camera tracking problem. However, most of the
effort has gone towards improving accuracy on high-quality benchmark datasets
while disregarding lower camera resolutions, compression artifacts and the
overwhelming amount of computational power and time needed to carry out this
task on its edge and thus making it prohibitive for large-scale and real-time
deployment. Therefore, in this work we shed light on practical issues that
should be addressed for the design of a multi-camera tracking system to provide
actionable and timely insights. Moreover, we propose a real-time city-scale
multi-camera vehicle tracking system that compares favorably to computationally
intensive alternatives and handles real-world, low-resolution CCTV instead of
idealized and curated video streams. To show its effectiveness, in addition to
integration into the Regional Integrated Transportation Information System
(RITIS), we participated in the 2021 NVIDIA AI City multi-camera tracking
challenge and our method is ranked among the top five performers on the public
leaderboard.
