Real-time people and vehicle detection from UAV imagery

Abstract

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%.