Is First Person Vision Challenging for Object Tracking? The TREK-100 Benchmark Dataset

Understanding human-object interactions is fundamental in First Person Vision (FPV). Tracking algorithms which follow the objects manipulated by the camera wearer can provide useful information to effectively model such interactions. Despite a few previous attempts to exploit trackers in FPV applications, a systematic analysis of the performance of state-of-the-art trackers in this domain is still missing. On the other hand, the visual tracking solutions available in the computer vision literature have significantly improved their performance in the last years for a large variety of target objects and tracking scenarios. To fill the gap, in this paper, we present TREK-100, the first benchmark dataset for visual object tracking in FPV. The dataset is composed of 100 video sequences densely annotated with 60K bounding boxes, 17 sequence attributes, 13 action verb attributes and 29 target object attributes. Along with the dataset, we present an extensive analysis of the performance of 30 among the best and most recent visual trackers. Our results show that object tracking in FPV is challenging, which suggests that more research efforts should be devoted to this problem.