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
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
Analysis of Gait Using a Treadmill and a Time-of-Flight Camera
Dyn3D '09 Proceedings of the DAGM 2009 Workshop on Dynamic 3D Imaging
Real-time upper-body human pose estimation using a depth camera
MIRAGE'11 Proceedings of the 5th international conference on Computer vision/computer graphics collaboration techniques
Human skeleton tracking from depth data using geodesic distances and optical flow
Image and Vision Computing
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An algorithm is created, which performs human gait analysis using spatial data and amplitude images from a Time-of-flight camera. For each frame in a sequence the camera supplies cartesian coordinates in space for every pixel. By using an articulated model the subject pose is estimated in the depth map in each frame. The pose estimation is based on likelihood, contrast in the amplitude image, smoothness and a shape prior used to solve a Markov random field. Based on the pose estimates, and the prior that movement is locally smooth, a sequential model is created, and a gait analysis is done on this model. The output data are: Speed, Cadence (steps per minute), Step length, Stride length (stride being two consecutive steps also known as a gait cycle), and Range of motion (angles of joints). The created system produces good output data of the described output parameters and requires no user interaction.