Joint Lightweight Object Tracking and Detection for Unmanned Vehicles

摘要

In this paper, we address the problem of lightweight and effective visual object tracking and we present a real-time tracking system suitable for integration in embedded autonomous platforms. We propose a novel tracking framework for classification-based re-detection and tracking, with learnable management of tracking and detection results. The proposed framework includes a novel, very efficient object reidentification method, which filters the detection candidates and systematically corrects the tracking results. In our experiments, we demonstrate the effectiveness of the proposed system by comparing its performance against several other state-of-the art trackers and report the results on the UAV123 and UAV20L datasets. The results indicate that the proposed method is significantly more robust and accurate against recent state-of-the-art trackers, surpassing problems caused by real-world scenarios, while maintaining fast tracking speeds, making it suitable for use in real-time vision applications for autonomous robots, such as Unmanned Aerial Vehicles (UAVs).