In this paper, we address the problem of long-term visual object tracking and we present an efficient real-time single object tracking system suitable for integration in autonomous platforms that need to encompass intelligent capabilities. We propose a novel long-term tracking framework for classification based re-detection and tracking, that incorporates state estimation, object re-identification and automated management of tracking and detection results. Our method integrates a novel object re-identification technique which efficiently filters a number of detection candidates and systematically corrects the tracking results. Through extensive experimental validation on the UAV123, UAV20L and TLP datasets, we demonstrate the effectiveness of the proposed system and its advantage over several state-of-the art trackers. The results furthermore highlight the proposed tracker’s ability to handle challenges arising from real-world and long-term scenarios, such as variations in pose, scale, occlusions and out-of-view situations. Furthermore, we propose a variant that is suitable for deployment on autonomous robots, such as Unmanned Aerial Vehicles.

在本文中，我们解决了长期视觉对象跟踪的问题，并且我们提出了一种高效的实时单对象跟踪系统，该系统适用于需要包含智能功能的自主平台中的集成。我们为基于分类的重新检测和跟踪提出了一种新颖的长期跟踪框架，该框架结合了状态估计，对象重新识别以及跟踪和检测结果的自动管理。我们的方法集成了一种新颖的对象重新识别技术，该技术可以有效过滤大量检测候选对象并系统地校正跟踪结果。通过对UAV123，UAV20L和TLP数据集进行广泛的实验验证，我们证明了所提出系统的有效性及其相对于几种先进跟踪器的优势。结果进一步突出了拟议的跟踪器应对现实世界和长期情景（例如姿势，比例，遮挡和视线之外的变化）带来的挑战的能力。此外，我们提出了一种适用于部署在自动驾驶机器人（例如无人飞行器）上的变体。