Traffic-Aware Multi-Camera Tracking of Vehicles Based on ReID and Camera Link Model

TL;DR

This paper introduces a comprehensive multi-camera vehicle tracking framework that combines traffic-aware single camera tracking, a trajectory-based camera link model, and hierarchical clustering to improve vehicle re-identification across cameras, achieving state-of-the-art results.

Contribution

It presents a novel traffic-aware tracking algorithm, a trajectory-based camera link model, and a hierarchical clustering method for enhanced multi-camera vehicle tracking.

Findings

Achieves a new state-of-the-art IDF1 score of 74.93% on CityFlow dataset.

Effectively integrates appearance, geometric features, and traffic scenarios for vehicle tracking.

Demonstrates improved cross-camera vehicle trajectory accuracy.

Abstract

Multi-target multi-camera tracking (MTMCT), i.e., tracking multiple targets across multiple cameras, is a crucial technique for smart city applications. In this paper, we propose an effective and reliable MTMCT framework for vehicles, which consists of a traffic-aware single camera tracking (TSCT) algorithm, a trajectory-based camera link model (CLM) for vehicle re-identification (ReID), and a hierarchical clustering algorithm to obtain the cross camera vehicle trajectories. First, the TSCT, which jointly considers vehicle appearance, geometric features, and some common traffic scenarios, is proposed to track the vehicles in each camera separately. Second, the trajectory-based CLM is adopted to facilitate the relationship between each pair of adjacently connected cameras and add spatio-temporal constraints for the subsequent vehicle ReID with temporal attention. Third, the hierarchical clustering algorithm is used to merge the vehicle trajectories among all the cameras to obtain the final MTMCT results. Our proposed MTMCT is evaluated on the CityFlow dataset and achieves a new state-of-the-art performance with IDF1 of 74.93%.