Multi-Vehicle Multi-Camera Tracking with Graph-Based Tracklet Features
- verfasst von
- 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.
- Organisationseinheit(en)
-
Forschungszentrum L3S
- Externe Organisation(en)
-
University of Tennessee, Chattanooga
- Typ
- Artikel
- Journal
- IEEE transactions on multimedia
- Band
- 26
- Seiten
- 972-983
- Anzahl der Seiten
- 12
- ISSN
- 1520-9210
- Publikationsdatum
- 2024
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Signalverarbeitung, Elektrotechnik und Elektronik, Medientechnik, Angewandte Informatik
- Ziele für nachhaltige Entwicklung
- SDG 11 – Nachhaltige Städte und Gemeinschaften
- Elektronische Version(en)
-
https://doi.org/10.1109/TMM.2023.3274369 (Zugang:
Geschlossen)