Personalization and Evaluation of a Real-Time Depth-Based Full Body Tracker

Reconstructing a three-dimensional representation of human motion in real-time constitutes an important research topic with applications in sports sciences, human-computer-interaction, and the movie industry. In this paper, we contribute with a robust algorithm for estimating a personalized human body model from just two sequentially captured depth images that is more accurate and runs an order of magnitude faster than the current state-of-the-art procedure. Then, we employ the estimated body model to track the pose in real-time from a stream of depth images using a tracking algorithm that combines local pose optimization and a stabilizing dataBase look-up. Together, this enables accurate pose tracking that is more accurate than previous approaches. As a further contribution, we evaluate and compare our algorithm to previous work on a comprehensive benchmark dataset containing more than 15 minutes of challenging motions. This dataset comprises calibrated marker-Based motion capture data, depth data, as well as ground truth tracking results and is publicly available for research purposes.