View-independent prediction of body dimensions in crowded environments

Abstract

This paper considers the problem of inferring the dimensions of non-visible body parts from images of incomplete bodies. This situation often occurs in CCTV videos of crowded scenes where people are mostly occluded. The approach we present relies on the ability to measure an observable body part which correlates to a missing body part. Anthropometric regression equations are then used to predict the dimension of the sought body part from the observable one. The example application of the paper considers acquiring a person’s head height to infer their stature. It is shown how a judicious selection of anthropometric points enables computation of the head height from any perspective images taken in uncontrolled environments with uncooperative subjects. Two regression models are proposed to infer stature from head height. Three real-life case studies have been chosen to assess the performance of our method on subjects observed in low resolution images and under various poses. Results show that the proposed method can yield statures of comparable accuracy to truth and two geometric methods.