Fast Pose Estimation with Parameter-Sensitive Hashing
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markerless human body motion capture using Markov random field and dynamic graph cuts
The Visual Computer: International Journal of Computer Graphics
Fast detection of marker pixels in video-based motion capture systems
Pattern Recognition Letters
Analyzing Gait Using a Time-of-Flight Camera
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
POSECUT: simultaneous segmentation and 3D pose estimation of humans using dynamic graph-cuts
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
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We present a system that analyzes human gait using a treadmill and a Time-of-flight camera. The camera provides spatial data with local intensity measures of the scene, and data are collected over several gait cycles. These data are then used to model and analyze the gait. For each frame the spatial data and the intensity image are used to fit an articulated model to the data using a Markov random field. To solve occlusion issues the model movement is smoothened providing the missing data for the occluded parts. The created model is then cut into cycles, which are matched and through Fourier fitting a cyclic model is created. The output data are: Speed, Cadence, Step length and Range-of-motion . The described output parameters are computed with no user interaction using a setup with no requirements to neither background nor subject clothing.