Outdoor Human Motion Capture by Simultaneous Optimization of Pose and Camera Parameters

• Published in: Computer graphics forum Vol. 34; no. 6; pp. 86 - 98
• Main Authors: Elhayek, A., Stoll, C., Kim, K. I., Theobalt, C.
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.09.2015
• Subjects: Algorithms; Analysis; Cameras; Consumption; Digital video; I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism-Animation; Image processing systems; Inertial; markerless human motion capture; Mathematical models; Motion capture; Movement; moving cameras; Outdoor; outdoor capture; Sensors; Skeletal system; Studies; Tracking
• ISSN: 0167-7055; 1467-8659
• DOI: 10.1111/cgf.12519

We present a method for capturing the skeletal motions of humans using a sparse set of potentially moving cameras in an uncontrolled environment. Our approach is able to track multiple people even in front of cluttered and non‐static backgrounds, and unsynchronized cameras with varying image quality and frame rate. We completely rely on optical information and do not make use of additional sensor information (e.g. depth images or inertial sensors). Our algorithm simultaneously reconstructs the skeletal pose parameters of multiple performers and the motion of each camera. This is facilitated by a new energy functional that captures the alignment of the model and the camera positions with the input videos in an analytic way. The approach can be adopted in many practical applications to replace the complex and expensive motion capture studios with few consumer‐grade cameras even in uncontrolled outdoor scenes. We demonstrate this based on challenging multi‐view video sequences that are captured with unsynchronized and moving (e.g. mobile‐phone or GoPro) cameras. We present a method for capturing the skeletal motions of humans using a sparse set of potentially moving cameras in an uncontrolled environment. Our approach is able to track multiple people even in front of cluttered and non‐static backgrounds, and unsynchronized cameras with varying image quality and frame rate. We completely rely on optical information and do not make use of additional sensor information (e.g. depth images or inertial sensors). Our algorithm simultaneously reconstructs the skeletal pose parameters of multiple performers and the motion of each camera. This is facilitated by a new energy functional that captures the alignment of the model and the camera positions with the input videos in an analytic way. The approach can be adopted in many practical applications to replace the complex and expensive motion capture studios with few consumer‐grade cameras even in uncontrolled outdoor scenes. We demonstrate this based on challenging multi‐view video sequences that are captured with unsynchronized and moving (e.g. mobile‐phone or GoPro) cameras.