A Comparison of Features for Regression-based Driver Head Pose Estimation under Varying Illumination Conditions

Head pose estimation provides key information about driver activity and awareness. Prior comparative studies are limited to temporally consistent illumination conditions under the assumption of brightness constancy. By contrast the illumination conditions inside a moving vehicle vary considerably with environmental conditions. In this study we present a base comparison of three features for head pose estimation, via support vector machine regression, based on Histogram of Oriented Gradient (HOG) features, Gabor filter responses and Active Shape Model (ASM) landmark features. These, reputedly illumination invariant, are presented through a common face localization framework from which we estimate driver head pose in two degrees-of-freedom and compare against a baseline approach for recovering head pose via weak perspective geometry. Evaluation is performed over a number of invehicle sequences, exhibiting uncontrolled illumination variation, in addition to ground truth data-sets, with controlled illumination changes, upon which we achieve a minimal ∼12° and ∼15° mean error in pitch and yaw respectively via ASM landmark features

Real-time People and Vehicle Detection from UAV Imagery

A generic and robust approach for the real-time detection of people and vehicles from an Unmanned Aerial Vehicle(UAV) is an important goal within the framework of fully autonomous UAV deployment for aerial reconnaissance andsurveillance. Here we present an approach for the automatic detection of vehicles based on using multiple trainedcascaded Haar classifiers with secondary confirmation in thermal imagery. Additionally we present a related approachfor people detection in thermal imagery based on a similar cascaded classification technique combining additionalmultivariate Gaussian shape matching. The results presented show the successful detection of vehicle and people undervarying conditions in both isolated rural and cluttered urban environments with minimal false positive detection.Performance of the detector is optimized to reduce the overall false positive rate by aiming at the detection of each objectof interest (vehicle/ person) at least once in the environment (i.e. per search patter flight path) rather than every object ineach image frame. Currently the detection rate for people is ~70% and cars ~80% although the overall episodic objectdetection rate for each flight pattern exceeds 90%

Multi-Modal Target Detection for Autonomous Wide Area Search and Surveillance

Generalised wide are search and surveillance is a common-place tasking for multi-sensory equipped autonomous systems. Here we present on a key supporting topic to this task- the automatic interpretation, fusion and detected target reporting from multi-modal sensor information received from multiple autonomous platforms deployed for wide-area environment search. We detail the realization of a real-time methodology for the automated detection of people and vehicles using combined visible-band (EO), thermal-band (IR) and radar sensing from a deployed network of multiple autonomous platforms (ground and aerial). This facilities real-time target detection, reported with varying levels of confidence, using information from both multiple sensors and multiple sensor platforms to provide environment-wide situational awareness. A range of automatic classification approaches are proposed, driven by underlying machine learning techniques, that facilitate the automatic detection of either target type with cross-modal target confirmation. Extended results are presented that show both the detection of people and vehicles under varying conditions in both isolated rural and cluttered urban environments with minimal false positive detection. Performance evaluation is presented at an episodic level with individual classifiers optimized for maximal each object of interest (vehicle/person) detection over a given search path/pattern of the environment, across all sensors and modalities, rather than on a per sensor sample basis. Episodic target detection, evaluated over a number of wide-area environment search and reporting tasks, generally exceeds 90%+ for the targets considered here. 1

Human Pose Classification within the Context of Near-IR Imagery Tracking

We address the challenge of human behaviour analysis within automated image understanding. Whilst prior work concentrates on this task within visible-band (EO) imagery, by contrast we target basic human pose classification in thermal-band (infrared, IR) imagery. By leveraging the key advantages of limb localization this imagery offers we target two distinct human pose classification problems of varying complexity: 1) identifying passive or active individuals within the scene and 2) the identification of individuals potentially carrying weapons. Both approaches use a discrete set of features capturing body pose characteristics from which a range of machine learning techniques are then employed for final classification. Significant success is shown on these challenging tasks over a wide range of environmental conditions within the wider context of automated human target tracking in thermal-band (IR) imagery. 1