On the Compensation for the Effects of Occlusion in Fronto-normal Gait Signal Processing

In surveillance applications, human gait data obtained from video contains idiosyncratic tendencies which allows it to be used as a biometric. This gait data has both time and image information. Expertise in the domain of time series analysis can be fruitfully employed in the image processing domain. In this paper, we consider the monocular frontal view of gait. In this view we track body parts to obtain time information and in doing so, complete occlusion of body parts may occur. To compensate for this, we present a novel standpoint where occluded images of objects may be considered as data missing from a time series. Thus we can consider this as a new application of the ¿missing data¿ problem studied in other fields dealing with time series data applied to the classic computer vision problem of occlusion. Using this approach, we consider three ways of compensating for occlusion - namely polynomial interpolation, autoregressive prediction and coupled time/frequency domain interpolation. We propose an experimental instantiation using a gait dataset and analyzing the motion of colored markers attached to body parts. The actual and predicted positions are compared which show our approach holds promise for complete occlusion compensation.