Transformer-based multi-object tracking algorithm for UAV
In response to the challenges faced in UAV multi-object tracking,including target occlusion,scale variations,rapid movements,and complex environments,this study introduces a UAV multi-object tracking algorithm based on the Transformer architecture.It leverages the Focal Transformer to capture both local and global interactions within the Transformer layers for high-resolution input.This algorithm is capable of generating target detection information and appearance features,thereby significantly enhancing tracking performance.For trajectory prediction,it incorporates the Kalman filtering method to accurately forecast target motion paths,contributing to improved tracking accuracy and robustness.In the data association process,it simultaneously considers three factors:detection confidence,appearance embedding distance,and IOU distance.This enhances the robustness of the multi-object tracking model and enables it to better track targets in complex scenarios.Furthermore,a secondary matching approach for trajectories is employed to further boost the algorithm's performance.Comparative validation on the VisDrone and UAVDT datasets demonstrates the effectiveness and feasibility of this algorithm in practical applications.This research presents a novel solution for UAV multi-object tracking,with promising applications across a wide range of scenarios.
multi object trackingtransformerKalman filteringdetection confidencemultiple feature matching
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