STCMOT: Spatio-Temporal Cohesion Learning for UAV-Based Multiple Object Tracking

Ma J, Tang C, Wu F, Zhao C, Zhang J, Xu Z (2024)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2024

Publisher: IEEE

Pages Range: 1-6

Conference Proceedings Title: IEEE International Conference on Multimedia and Expo (ICME)

Event location: Niagara Falls, ON, Canada CA

ISBN: 979-8-3503-9015-5

URI: https://ieeexplore.ieee.org/document/10688174

DOI: 10.1109/ICME57554.2024.10688174

Abstract

Multiple object tracking (MOT) in Unmanned Aerial Vehicle (UAV) videos is important for diverse applications in computer vision. Current MOT trackers rely on accurate object detection results and precise matching of target reidentification (ReID). These methods focus on optimizing target spatial attributes while overlooking temporal cues in modelling object relationships, especially for challenging tracking conditions such as object deformation and blurring, etc. To address the above-mentioned issues, we propose a novel Spatio-Temporal Cohesion Multiple Object Tracking framework (STCMOT), which utilizes historical embedding features to model the representation of ReID and detection features in a sequential order. Concretely, a temporal embedding boosting module is introduced to enhance the discriminability of individual embedding based on adjacent frame cooperation. While the trajectory embedding is then propagated by a temporal detection refinement module to mine salient target locations in the temporal field. Extensive experiments on the VisDrone2019 and UAVDT datasets demonstrate our STCMOT sets a new state-of-the-art performance in MOTA and IDF1 metrics. The source codes are released at https://github.com/ydhcg-BoBo/STCMOT.

Authors with CRIS profile

Fei Wu Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

University of Chinese Academy of Sciences (UCAS) / 中国科学院大学 CN China (CN)

How to cite

APA:

Ma, J., Tang, C., Wu, F., Zhao, C., Zhang, J., & Xu, Z. (2024). STCMOT: Spatio-Temporal Cohesion Learning for UAV-Based Multiple Object Tracking. In IEEE International Conference on Multimedia and Expo (ICME) (pp. 1-6). Niagara Falls, ON, Canada, CA: IEEE.

MLA:

Ma, Jianbo, et al. "STCMOT: Spatio-Temporal Cohesion Learning for UAV-Based Multiple Object Tracking." Proceedings of the 2024 IEEE International Conference on Multimedia and Expo (ICME), Niagara Falls, ON, Canada IEEE, 2024. 1-6.

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