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

Abstract

Further information on publisher's website:http://spie.org/ESD/conferencedetails/optics-and-photonics-for-counterterrorism-crime-ghting-and-defence Publisher's copyright statement:Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. ABSTRACTTarget 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.

Toward sensor modular autonomy for persistent land intelligence surveillance and reconnaissance (ISR)

“…A detailed description of the target classification work has been previously published 7,8 , but in brief this ASM employs probabilistic detection/classification, passive geo-location via a technique that is independent of variations in target posture (i.e. behaviour) and within the statistical error bounds for pedestrian height posture, and Support Vector Machine (SVM) regression based pedestrian posture estimation operating on Histogram of Orientated Gradient (HOG) feature descriptors.…”

Real-time classification of vehicles by type within infrared imagery

“…The work presented in this paper is a direct extension of [1,2] that demonstrated photogrammetric pedestrian localization within thermal-band imagery incorporating a lightweight tracking solution akin to that of [4]. Building directly on this framework presented in [1], here we present a method that additionally facilitates the passive localization of vehicles within thermal-band (IR) imagery based on prior classification vehicle type.…”

“…A real-time approach for the detection, classification and localization of pedestrian targets via thermal-band (infra-red) sensing was presented with supporting statistical evidence underpinning the key photogrammetric assumptions. Subsequent work in [2] explicitly addressed the remaining issue of correcting for pedestrian posture variation within this localization context. By contrast, here we present an approach for the automatic classification of vehicles by sub-type, such that a similar photogrammetric localization and tracking strategy can be employed.…”

“…Within the context of automated visual surveillance from infra-red imagery, our prior work on pedestrians [1,2] demonstrated that reasonable performance can practically be achieved through the combined use of infra-red imagery (thermal-band, spectral range: 8-12µm) and the application of real-time photogrammetry. A key advantage of such thermal-band infrared (IR) imagery for pedestrian localization is robust detection of human shape signatures within the scene [3][4][5].…”

“…Building directly on this framework presented in [1], here we present a method that additionally facilitates the passive localization of vehicles within thermal-band (IR) imagery based on prior classification vehicle type. Specifically we evaluate a range of feature detector/descriptor combinations with a traditional featuredriven bag of visual words architecture (akin to [1]), the use of histogram of oriented gradient features within a similar classification framework (building on [2]) and finally two modern convolutional neural network (CNN) architectures (AlexNet [25], GoogLeNet [26]). …”