Abstract
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.
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References
Artts (2009), http://www.artts.eu
Mesa (2009), http://www.mesa-imaging.ch
Alkjaer, E.B., Simonsen, T., Dygre-Poulsen, P.: Comparison of inverse dynamics calculated by two- and three-dimensional models during walking. In: 2001 Gait and Posture, pp. 73–77 (2001)
Besag, J.: On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society. Series B (Methodological) 48(3), 259–302 (1986)
Bray, M., Kohli, P., Torr, P.H.S.: Posecut: simultaneous segmentation and 3D pose estimation of humans using dynamic graph-cuts. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3952, pp. 642–655. Springer, Heidelberg (2006)
Jensen, R.R., Paulsen, R.R., Larsen, R.: Analyzing gait using a time-of-flight camera. In: Salberg, A.B., Hardeberg, J.Y., Jenssen, Q. (eds.) SCIA 2009. LNCS, vol. 5575, pp. 21–30. Springer, Heidelberg (2009)
Kolmogorov, V., Zabin, R.: What energy functions can be minimized via graph cuts? IEEE Transactions on Pattern Analysis and Machine Intelligence 26(2), 147–159 (2004)
Medina-Carnicer, R., Garrido-Castro, J.L., Collantes-Estevez, E., Martinez-Galisteo, A.: Fast detection of marker pixels in video-based motion capture systems. Pattern Recognition Letters 30(4), 432–439 (2009)
Shakhnarovich, G., Viola, P., Darrell, T.: Fast pose estimation with parameter-sensitive hashing. In: Proceedings Ninth IEEE International Conference on Computer Vision, vol. 2, pp. 750–757 (2003)
Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), vol. 2, pp. 246–252 (1999), ISSN 10636919
Wan, C., Yuan, B., Miao, Z.: Markerless human body motion capture using Markov random field and dynamic graph cuts. Visual Computer 24(5), 373–380 (2008)
Ye, Q.-Z.: The signed Euclidean distance transform and its applications. In: 1988 Proceedings of 9th International Conference on Pattern Recognition, vol. 1, pp. 495–499 (1988)
Zhu, Y., Dariush, B., Fujimura, K.: Controlled human pose estimation from depth image streams. In: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPR Workshops), pp. 1–8 (2008)
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Jensen, R.R., Paulsen, R.R., Larsen, R. (2009). Analysis of Gait Using a Treadmill and a Time-of-Flight Camera. In: Kolb, A., Koch, R. (eds) Dynamic 3D Imaging. Dyn3D 2009. Lecture Notes in Computer Science, vol 5742. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03778-8_12
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DOI: https://doi.org/10.1007/978-3-642-03778-8_12
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