Robot-Person Tracking in Uniform Appearance Scenarios: A New Dataset and Challenges

Abstract

Person-tracking robots have many applications including security, surveillance, and autonomous driving. Despite the abundance of uniform appearance in many contexts and the challenges they exhibit, there is a lack of video datasets dedicated to benchmarking tracking algorithms in such contexts. In this article, we propose a new high-quality RGB-D benchmark called PTUA for robot–person tracking in uniform appearance scenarios. PTUA is recorded using an RGB-D sensor on top of a moving robot and consists of 45 sequences containing more than 85 K frames. Each frame is manually annotated with a bounding box and attributes, making PTUA the largest and the most challenging person tracking RGB-D dataset. To the best of our knowledge, such a densely annotated and properly synchronized RGB-D tracking benchmark does not exist in the literature. Each sequence comprises various challenges deriving from real-life scenarios where the target person appears highly similar to the background or distractors. By releasing PTUA, we expect to provide the community with a large-scale challenging RGB-D benchmark with high quality for the robust evaluation of trackers on uniform appearance scenarios for autonomous robots. We also present a rigorous experimental evaluation of the state-of-the-art trackers on the PTUA dataset with a comprehensive analysis. The findings evidence the challenges of person tracking in a uniform appearance scenario for both target tracking and robot–person tracking, and the need to bridge the performance gap. In addition, we propose a new RGB-D tracker that extracts features from RGB-D frames and it achieves the best performance on each challenging scenario of PTUA.