Posture Estimation for Improved Photogrammetric Localization of Pedestrians in Monocular Infrared Imagery

Abstract

Target tracking within conventional video imagery poses a significant challenge that is increasingly being addressed via complex algorithmic solutions. The complexity of this problem can be fundamentally attributed to the ambiguity associated with actual 3D scene position of a given tracked object in relation to its observed position in 2D image space. Recent work has tackled this challenge head on by returning to classical photogrammetry, within the context of current target detection and classification techniques, as a means of recovering the true 3D position of pedestrian targets within the bounds of current accuracy norms. A key limitation in such approaches is the assumption of posture – that the observed pedestrian is at full height stance within the scene. Whilst prior work has shown the effects of statistical height variation to be negligible, variations in the posture of the target may still pose a significant source of potential error. Here we present a method that addresses this issue via the use of regression based pedestrian posture estimation. This is demonstrated for variations in pedestrian target height ranging from 0.4-2m over a distance to target range of 7-30m.