MULTI-OBJECT TRACKING FOR UNMANNED AERIAL VEHICLES BASED ON MULTIFRAME FEATURE FUSION

To address the issues of tracking trajectory loss caused by small object size, frequent view angle changes and object occlusion in the multi-object tracking task of Unmanned Aerial Vehicle (UAV), in this paper, we propose a multi-object tracker for UAV based on multi-frame feature fusion. First, in order to more fully extract and utilize the interframe information, we design an attention-based adaptive multi-frame fusion module, which introduces Efficient Channel Attention (ECA) to trade-off the importance of the information in the history frames and the current frame. Second, we use a high-resolution feature extraction network as backbone network to extract features. The proposed method is evaluated on the UAV multi-object tracking datasets of Visdrone2019 and UAVDT. Compared with other mainstream multi-object tracking algorithms, our method achieves higher accuracy and fewer identity switches, which effectively improves multi-object tracking performance.