Browsing by Author "Gaszczak, Anna"

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    A non-temporal texture driven approach to real-time fire detection
    (2011-09-14T00:00:00Z) Chenebert, Audrey; Breckon, Toby P.; Gaszczak, Anna
    Here we investigate the automatic detection of fire pixel regions in conventional video (or still) imagery within realtime bounds. As an extension to prior, established approaches within this field we specifically look to extend the primary use of threshold-driven colour spectroscopy to the combined use of colour-texture feature descriptors as an input to a trained classification approach that is independent of temporal information. We show the limitations of such spectroscopy driven approaches on simple, real-world examples and propose our novel extension as a robust, real-time solution within this field by combining simple texture descriptors to illustrate maximal ∼98% fire region detecti
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    ItemOpen Access
    Real-time people and vehicle detection from UAV imagery
    (2011-01-24T00:00:00Z) Gaszczak, Anna; Breckon, Toby P.; Han, Jiwan; Röning, J.; Casasent, D. P.; Hall, E. L.
    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%.