The visual analysis of human movement: a survey
Computer Vision and Image Understanding
A morphable model for the synthesis of 3D faces
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
3D Statistical Shape Models Using Direct Optimisation of Description Length
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Recognizing and Tracking Human Action
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Kinematic jump processes for monocular 3D human tracking
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Covered body analysis in application to patient monitoring
CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
An architecture for a self configurable video supervision
AREA '08 Proceedings of the 1st ACM workshop on Analysis and retrieval of events/actions and workflows in video streams
Robust Pose Recognition of the Obscured Human Body
International Journal of Computer Vision
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A lot of computer vision applications have to deal with occlusions. In such settings only a subset of the features of interest can be observed, i.e. only incomplete or partial measurements are available. In this article we show how a learned statistical model can be used to make a prediction of the unknown (occluded) features. The probabilistic nature of the framework also allows to compute the remaining uncertainty given an incomplete observation. The resulting posterior probability distribution can then be used for inference. Additional unknowns such as alignment or scale are easily incorporated into the framework. Instead of computing the alignment in a preprocessing step, it is left as an additional uncertainty, similar to the uncertainty introduced by the missing values of the measurement. It is shown how the technique can be applied to the analysis of human loco-motion, when body parts are occluded. Experiments show how the unobserved body locations are predicted and how it can be inferred whether the measurements come from a running or walking sequence.