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)