Multiview depth-based pose estimation

Commonly used human motion capture systems require intrusive attachment of markers that are visually tracked with multiple cameras. In this work we present an efficient and inexpensive solution to markerless motion capture using only a few Kinect sensors. We use our system to design a smart home platform with a network of Kinects that are installed inside the house. Our first contribution is a multiview pose estimation system. Unlike the previous work on 3d pose estimation using a single depth camera, we relax constraints on the camera location and do not assume a co-operative user. We apply recent image segmentation techniques with convolutional neural networks to depth images and use curriculum learning to train our system on purely synthetic data. Our method accurately localizes body parts without requiring an explicit shape model. The body joint locations are then recovered by combining evidence from multiple views in real-time. Our second contribution is a dataset of 6 million synthetic depth frames for pose estimation from multiple cameras with varying levels of complexity to make curriculum learning possible. We show the efficacy and applicability of our data generation process through various evaluations. Our final system exceeds the state-of-the-art results on multiview pose estimation on the Berkeley MHAD dataset. Our third contribution is a scalable software platform to coordinate Kinect devices in real-time over a network. We use various compression techniques and develop software services that allow communication with multiple Kinects through TCP/IP. The flexibility of our system allows real-time orchestration of up to 10 Kinect devices over Ethernet.Science, Faculty ofComputer Science, Department ofGraduat