City-Scale Multi-Camera Vehicle Tracking System with Improved Self-Supervised Camera Link Model

TL;DR

This paper presents a city-scale multi-camera vehicle tracking system that employs a self-supervised model to automatically establish camera links, significantly improving efficiency and accuracy without manual annotations.

Contribution

The novel self-supervised camera link model automatically extracts multi-camera relationships, reducing deployment time and costs compared to manual annotation methods.

Findings

Achieves 61.07% IDF1 Score on CityFlow V2 benchmarks.

Outperforms existing automatic camera-link based methods.

Reduces manual effort in multi-camera vehicle tracking.

Abstract

Multi-Target Multi-Camera Tracking (MTMCT) has broad applications and forms the basis for numerous future city-wide systems (e.g. traffic management, crash detection, etc.). However, the challenge of matching vehicle trajectories across different cameras based solely on feature extraction poses significant difficulties. This article introduces an innovative multi-camera vehicle tracking system that utilizes a self-supervised camera link model. In contrast to related works that rely on manual spatial-temporal annotations, our model automatically extracts crucial multi-camera relationships for vehicle matching. The camera link is established through a pre-matching process that evaluates feature similarities, pair numbers, and time variance for high-quality tracks. This process calculates the probability of spatial linkage for all camera combinations, selecting the highest scoring pairs to create camera links. Our approach significantly improves deployment times by eliminating the need for human annotation, offering substantial improvements in efficiency and cost-effectiveness when it comes to real-world application. This pairing process supports cross camera matching by setting spatial-temporal constraints, reducing the searching space for potential vehicle matches. According to our experimental results, the proposed method achieves a new state-of-the-art among automatic camera-link based methods in CityFlow V2 benchmarks with 61.07% IDF1 Score.