Multi-Vehicle Multi-Camera Tracking with Graph-Based Tracklet Features

authored by

Tuan T. Nguyen, Hoang H. Nguyen, Mina Sartipi, Marco Fisichella

Abstract

Multi-target multi-camera tracking (MTMCT) is an important application in intelligent transportation systems (ITS). The conventional works follow the tracking-by-detection scheme and use the information of the object image separately while matching the object from different cameras. As a result, the association information from the object image is lost. To utilize this information, we propose an efficient MTMCT application that builds features in the form of a graph and customizes graph similarity to match the vehicle objects from different cameras. We present algorithms for both the online scenario, where only the past images are used to match a vehicle object, and the offline scenario, where a given vehicle object is tracked with past and future images. For offline scenarios, our method achieves an IDF1-score of 0.8166 on the Cityflow dataset, which contains the actual scenes of the city from multiple street cameras. For online scenarios, our method achieves an IDF1-score of 0.75 with an FPS of 14.

Organisation(s)

L3S Research Centre

External Organisation(s)

University of Tennessee, Chattanooga

Type

Article

Journal

IEEE transactions on multimedia

Volume

26

Pages

972-983

No. of pages

12

ISSN

1520-9210

Publication date

2024

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Signal Processing, Electrical and Electronic Engineering, Media Technology, Computer Science Applications

Sustainable Development Goals

SDG 11 - Sustainable Cities and Communities

Electronic version(s)

https://doi.org/10.1109/TMM.2023.3274369 (Access: Closed)