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. Our codes and datasets are available at https://github.com/elituan/GraphBasedTracklet_MTMCT.
Description
Multi-Vehicle Multi-Camera Tracking with Graph-Based Tracklet Features | IEEE Journals & Magazine | IEEE Xplore
%0 Journal Article
%1 10121650
%A Nguyen, Tuan T.
%A Nguyen, Hoang H.
%A Sartipi, Mina
%A Fisichella, Marco
%D 2023
%J IEEE Transactions on Multimedia
%K myown #rank1 from:mfisichella
%P 1-13
%R 10.1109/TMM.2023.3274369
%T Multi-Vehicle Multi-Camera Tracking with Graph-Based Tracklet Features
%U https://ieeexplore.ieee.org/document/10121650
%X 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. Our codes and datasets are available at https://github.com/elituan/GraphBasedTracklet_MTMCT.
@article{10121650,
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. Our codes and datasets are available at https://github.com/elituan/GraphBasedTracklet_MTMCT.},
added-at = {2024-02-07T10:08:38.000+0100},
author = {Nguyen, Tuan T. and Nguyen, Hoang H. and Sartipi, Mina and Fisichella, Marco},
biburl = {https://www.bibsonomy.org/bibtex/2130568759a1c785da8a7bac011f764e4/l3s},
description = {Multi-Vehicle Multi-Camera Tracking with Graph-Based Tracklet Features | IEEE Journals & Magazine | IEEE Xplore},
doi = {10.1109/TMM.2023.3274369},
interhash = {b10a7d72c8021c49c7f62ee453be740b},
intrahash = {130568759a1c785da8a7bac011f764e4},
issn = {1941-0077},
journal = {IEEE Transactions on Multimedia},
keywords = {myown #rank1 from:mfisichella},
pages = {1-13},
timestamp = {2024-02-07T10:08:38.000+0100},
title = {Multi-Vehicle Multi-Camera Tracking with Graph-Based Tracklet Features},
url = {https://ieeexplore.ieee.org/document/10121650},
year = {2023 }
}