Real-time people and vehicle detection from UAV imagery

Anna Gaszczak; Toby P. Breckon; Jiwan Han

doi:10.1117/12.876663

24 January 2011 Real-time people and vehicle detection from UAV imagery

Anna Gaszczak, Toby P. Breckon, Jiwan Han

Proceedings Volume 7878, Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques; 78780B (2011) https://doi.org/10.1117/12.876663
Event: IS&T/SPIE Electronic Imaging, 2011, San Francisco Airport, California, United States

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 and surveillance. Here we present an approach for the automatic detection of vehicles based on using multiple trained cascaded Haar classifiers with secondary confirmation in thermal imagery. Additionally we present a related approach for people detection in thermal imagery based on a similar cascaded classification technique combining additional multivariate Gaussian shape matching. The results presented show the successful detection of vehicle and people under varying 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 object of interest (vehicle/person) at least once in the environment (i.e. per search patter flight path) rather than every object in each image frame. Currently the detection rate for people is ~70% and cars ~80% although the overall episodic object detection rate for each flight pattern exceeds 90%.

Citation Download Citation

Anna Gaszczak, Toby P. Breckon, and Jiwan Han "Real-time people and vehicle detection from UAV imagery", Proc. SPIE 7878, Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques, 78780B (24 January 2011); https://doi.org/10.1117/12.876663

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