Multi-Target Multi-Camera Tracking of Vehicles using Metadata-Aided Re-ID and Trajectory-Based Camera Link Model

TL;DR

This paper introduces a comprehensive framework for multi-camera vehicle tracking that combines metadata-aided re-identification, trajectory-based camera link modeling, and hierarchical clustering to improve accuracy and reduce candidate search space.

Contribution

The paper presents a novel integration of metadata features, TCLM, and a traffic-aware single-camera tracker for enhanced multi-target multi-camera vehicle tracking.

Findings

Achieved IDF1 score of 76.77% on CityFlow dataset.

Outperformed existing state-of-the-art MTMCT methods.

Demonstrated robustness of the proposed approach in complex scenarios.

Abstract

In this paper, we propose a novel framework for multi-target multi-camera tracking (MTMCT) of vehicles based on metadata-aided re-identification (MA-ReID) and the trajectory-based camera link model (TCLM). Given a video sequence and the corresponding frame-by-frame vehicle detections, we first address the isolated tracklets issue from single camera tracking (SCT) by the proposed traffic-aware single-camera tracking (TSCT). Then, after automatically constructing the TCLM, we solve MTMCT by the MA-ReID. The TCLM is generated from camera topological configuration to obtain the spatial and temporal information to improve the performance of MTMCT by reducing the candidate search of ReID. We also use the temporal attention model to create more discriminative embeddings of trajectories from each camera to achieve robust distance measures for vehicle ReID. Moreover, we train a metadata classifier for MTMCT to obtain the metadata feature, which is concatenated with the temporal attention based embeddings. Finally, the TCLM and hierarchical clustering are jointly applied for global ID assignment. The proposed method is evaluated on the CityFlow dataset, achieving IDF1 76.77%, which outperforms the state-of-the-art MTMCT methods.