Paper
9 January 2025 Spatiotemporal attention fusion network for multiple object tracking of unmanned aerial vehicle
Senlin Qin, Lei Jiang, Jianlin Zhang, Dongxu Liu, Meihui Li
Author Affiliations +
Proceedings Volume 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024); 1348604 (2025) https://doi.org/10.1117/12.3055793
Event: Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 2024, Chengdu, China
Abstract
The task of multiple object tracking from the perspective of Unmanned Aerial Vehicle (UAV) is becoming increasingly important and has a wide range of applications. However, conventional multiple object trackers do not fully exploit temporal and spatial information, facing challenges such as target blurring and variable trajectories due to the high-speed motion of UAV. In this paper, we propose STAF(Spatiotemporal Attention Fusion Network), which is based on spatiotemporal multi-head attention and fully integrates information from video sequence frames, enhancing the detection capability of targets. To better handle the camera shake, we develop an appearance feature update algorithm based confidence. The proposed method has demonstrated improvements on the VisDrone2019 dataset.
(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Senlin Qin, Lei Jiang, Jianlin Zhang, Dongxu Liu, and Meihui Li "Spatiotemporal attention fusion network for multiple object tracking of unmanned aerial vehicle", Proc. SPIE 13486, Fourth International Conference on Computer Vision, Application, and Algorithm (CVAA 2024), 1348604 (9 January 2025); https://doi.org/10.1117/12.3055793
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