@inproceedings{9e72152c0a97496cbf414dbd2f50ed5b,
title = "Multi-drone Multi-object Tracking with RGB Cameras Using Spatio-Temporal Cues",
abstract = "This paper proposed a method for multi-drone multi-object tracking (MDMOT) with spatio-temporal cues. When multiple cameras mounted on different drones are used to localize and track aerial objects, false associations between objects from different cameras will lead to the problem of false positive objects in the 3D space. Therefore, we first re-project all the triangulation localization points back to the camera pixel plane and calculate the re-projection errors for the construction of the spatial likelihood matrix of association. Then, the association likelihood is adjusted with temporal information by integrating the historical association results. Finally, we use the likelihood values as the similarity scores for data association. Our method relies only on distributed RGB cameras. The effectiveness is proved by quantitative and qualitative experiments with multi-object tracking metrics.",
keywords = "Data association, MDMOT, RGB Cameras, Triangulation",
author = "Guanyin Chen and Bohui Fang and Wenxing Fu and Tao Yang",
note = "Publisher Copyright: {\textcopyright} Beijing HIWING Scientific and Technological Information Institute 2024.; 3rd International Conference on Autonomous Unmanned Systems, ICAUS 2023 ; Conference date: 09-09-2023 Through 11-09-2023",
year = "2024",
doi = "10.1007/978-981-97-1091-1_38",
language = "英语",
isbn = "9789819710904",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "412--421",
editor = "Yi Qu and Mancang Gu and Yifeng Niu and Wenxing Fu",
booktitle = "Proceedings of 3rd 2023 International Conference on Autonomous Unmanned Systems (3rd ICAUS 2023) - Volume IV",
}