LAUSR

The advent of low-cost depth cameras, such as the Microsoft Kinect in the consumer market, has made many indoor applications and games based on motion tracking available to the everyday user. However, it is a large challenge to track human motion via such a camera because of its low-quality images, missing depth values, and noise. In this paper, we propose a novel human motion capture method based on a cooperative structure of multiple low-cost RGBD cameras, which can effectively avoid these problems. This structure can also manage the problem of body occlusions that appears when a single camera is used. Moreover, the whole process does not require training data, which makes this approach easily deployed and reduces operation time. We use the color image, depth image, and point cloud acquired in each view as the data source, and an initial pose is extracted in our optimization framework by aligning multiple point clouds from different cameras. The pose is dynamically updated by combining a filtering approach with a Markov model to estimate new poses in video streams. To verify the efficiency and robustness of our approach, we capture a wide variety of human actions via three cameras in indoor scenes and compare the tracking results of the proposed method to those of the current state-of-the-art methods. Moreover, our system is tested on more complex situations, in which multiple humans move within a scene, possibly occluding each other to some extent. The actions of multiple humans are tracked simultaneously, which would assist group behavior analysis.