Re-identification framework for long term visual object tracking based on object detection and classification

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.