Reconstruction of occluded ROI in multi-person gait based on numerical methods

Occlusion is an important factor for analysis of human gait recognition in real-time scenarios. In multi-person gait (MPG) or dynamic occlusion, gait recognition is affected due to occluded body parts known as region of interests (ROIs). The aim of this article is to reconstruct the occluded ROIs and measure the errors associated with the reconstruction methods. The contribution of this article is threefold: firstly, we segment five dynamic ROIs; secondly, reconstruction of ROIs using Lagrange, piecewise cubic hermite (PCH) and cubic spline and thirdly, a comparison among the above methods in MPG scenario. We consider the human body into two parts, i.e., lower and upper body. In lower body, we have considered ankle, while knee in upper body: wrist, elbow, and shoulder have been considered. The dataset used in this study consists of dynamic occlusion scenarios. The quantitative assessment of the above methods are based on four parameters such as mean square error, root mean square error, mean absolute error and mean absolute percentage error. Results show that PCH consistently outperforms the other methods in the reconstruction of occluded ROIs in MPG scenario.