Toward a 3D body part detection video dataset and hand tracking benchmark

The purpose of this paper is twofold. First, we introduce our Microsoft Kinect--based video dataset of American Sign Language (ASL) signs designed for body part detection and tracking research. This dataset allows researchers to experiment with using more than 2-dimensional (2D) color video information in gesture recognition projects, as it gives them access to scene depth information. Not only can this make it easier to locate body parts like hands, but without this additional information, two completely different gestures that share a similar 2D trajectory projection can be difficult to distinguish from one another. Second, as an accurate hand locator is a critical element in any automated gesture or sign language recognition tool, this paper assesses the efficacy of one popular open source user skeleton tracker by examining its performance on random signs from the above dataset. We compare the hand positions as determined by the skeleton tracker to ground truth positions, which come from manual hand annotations of each video frame. The purpose of this study is to establish a benchmark for the assessment of more advanced detection and tracking methods that utilize scene depth data. For illustrative purposes, we compare the results of one of the methods previously developed in our lab for detecting a single hand to this benchmark.