Scalable and Real-time Multi-Camera Vehicle Detection, Re-Identification, and Tracking

• URL: http://arxiv.org/abs/2204.07442v1
• Date: Fri, 15 Apr 2022 12:47:01 GMT
• Title: Scalable and Real-time Multi-Camera Vehicle Detection, Re-Identification, and Tracking
• Authors: Pirazh Khorramshahi, Vineet Shenoy, Michael Pack, Rama Chellappa
• Abstract summary: We propose a real-time city-scale multi-camera vehicle tracking system that handles real-world, low-resolution CCTV instead of idealized and curated video streams. Our method is ranked among the top five performers on the public leaderboard.
• Score: 58.95210121654722
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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