%0 Journal Article %1 10121650 %A Nguyen, Tuan T. %A Nguyen, Hoang H. %A Sartipi, Mina %A Fisichella, Marco %D 2024 %J IEEE Transactions on Multimedia %K myown MTMCT ITS vehicle from:erichoang #rank1 multi-camera tracking %P 972-983 %R 10.1109/TMM.2023.3274369 %T Multi-Vehicle Multi-Camera Tracking With Graph-Based Tracklet Features %V 26 %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.

@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.}, added-at = {2024-02-12T17:25:05.000+0100}, author = {Nguyen, Tuan T. and Nguyen, Hoang H. and Sartipi, Mina and Fisichella, Marco}, biburl = {https://www.bibsonomy.org/bibtex/21d59f9b1b65e3a4fbcc8dd6cb3713fdf/l3s}, doi = {10.1109/TMM.2023.3274369}, interhash = {cacebc30b612312657ad260572a5eb04}, intrahash = {1d59f9b1b65e3a4fbcc8dd6cb3713fdf}, issn = {1941-0077}, journal = {IEEE Transactions on Multimedia}, keywords = {myown MTMCT ITS vehicle from:erichoang #rank1 multi-camera tracking}, pages = {972-983}, timestamp = {2024-02-12T17:25:05.000+0100}, title = {Multi-Vehicle Multi-Camera Tracking With Graph-Based Tracklet Features}, volume = 26, year = 2024 }