Robust multiple car tracking with occlusion reasoning
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
A Probabilistic Exclusion Principle for Tracking Multiple Objects
International Journal of Computer Vision
Estimation of parameters and eigenmodes of multivariate autoregressive models
ACM Transactions on Mathematical Software (TOMS)
Human Identification Based on Gait (The Kluwer International Series on Biometrics)
Human Identification Based on Gait (The Kluwer International Series on Biometrics)
Estimation of missing markers in human motion capture
The Visual Computer: International Journal of Computer Graphics
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Gait is a relatively new biometric which shows promise in its use. In this paper, we examine the monocular frontal view of gait. When tracking body parts in this view, complete occlusion of body parts may occur. To compensate for this, we offer a fresh standpoint where occluded data may be considered as data missing from a time series. Thus we can consider this as a novel application of the "missing data" problem studied in other fields dealing with time series data. Using this approach, we consider three ways of coping with occlusion by using a gait dataset and analysing the motion of coloured markers attached to body parts. The occluded motions are compensated for and the actual and predicted positions are compared which show our approach has promise for coping with complete occlusion.