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Posture estimation for improved photogrammetric localization of pedestrians in monocular infrared imagery

Target tracking complexity within conventional video imagery 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, within thermal-band infrared imagery, has tackled this challenge head on by returning to classical photogrammetry as a means of recovering the true 3D position of pedestrian targets. 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 Support Vector Machine (SVM) regression based pedestrian posture estimation operating on Histogram of Orientated Gradient (HOG) feature descriptors. Within an existing tracking framework, we demonstrate improved target localization that is independent of variations in target posture (i.e. behaviour) and within the statistical error bounds of prior work for pedestrian height posture varying from 0.4-2.4m over a distance to target range of 7-30m.