MultiRPN-DIDNet: Multiple RPNs and Distance-IoU Discriminative Network for Real-Time UAV Target Tracking

Abstract

Target tracking in low-altitude Unmanned Aerial Vehicle (UAV) videos faces many technical challenges due to the relatively small sizes, various orientation changes of the objects and diverse scenes. As a result, the tracking performance is still not satisfactory. In this paper, we propose a real-time single-target tracking method with multiple Region Proposal Networks (RPNs) and Distance-Intersection-over-Union (Distance-IoU) Discriminative Network (DIDNet), namely MultiRPN-DIDNet, in which ResNet50 is used as the backbone network for feature extraction. Firstly, an instance-based RPN suitable for the target tracking task is constructed under the framework of Simases Neural Network. RPN is to perform bounding box regression and classification, in which channel attention mechanism is integrated to improve the representative capability of the deep features. The RPNs built on the Block 2, Block 3 and Block 4 of ResNet50 output their own Regression (Reg) coefficients and Classification scores (Cls) respectively, which are weighted and then fused to determine the high-quality region proposals. Secondly, a DIDNet is designed to correct the candidate target’s bounding box finely through the fusion of multi-layer features, which is trained with the Distance-IoU loss. Experimental results on the public datasets of UAV20L and DTB70 show that, compared with the state-of-the-art UAV trackers, the proposed MultiRPN-DIDNet can obtain better tracking performance with fewer region proposals and correction iterations. As a result, the tracking speed has reached 33.9 frames per second (FPS), which can meet the requirements of real-time tracking tasks.

SF-YOLOv5: Improved YOLOv5 with swin transformer and fusion-concat method for multi-UAV detection

“…14 In addition, some networks improve the accuracy of object detection by improving the method of loss function. 15 However, due to the characteristics of the multi-UAV and the scenes, such as the inconspicuous features among multiple UAVs, the interference of birds or flying objects, complex trajectories, various background environments and so on, the effective and reliable multi-UAV detection algorithm is demanded. By combining with swin transformer and fusion-concat method, SF-YOLOv5 detection algorithm based on YOLOv5 is proposed for the detection of multi-UAV.…”

“…14 In addition, some networks improve the accuracy of object detection by improving the method of loss function. 15…”