Bio-inspired looming object detection using event-based cameras

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dc.contributor.authorSánchez Attolini, Guillermo
dc.contributor.authorUniversity of Lethbridge. Faculty of Arts and Science
dc.contributor.supervisorCheng, Howard
dc.date.accessioned2020-02-20T23:39:28Z
dc.date.available2020-02-20T23:39:28Z
dc.date.issued2019
dc.degree.levelMastersen_US
dc.description.abstractWe present a novel looming object detection method for event-based cameras. Event-based cameras capture a scene without generating redundant information thus reducing the needed transmission power, bandwidth, and computations to process the redundant information generated by conventional frame-based cameras. Regular computer vision algorithms cannot be directly applied to them. Conversely, existing event-based algorithms that detect looming objects have some limitations. In this thesis, an existing bio-inspired looming object detection algorithm for frame-based cameras was adapted for an event-based camera. The adapted algorithm was then used as part of a novel looming object detection algorithm that is fast and capable of detecting multiple looming objects in a scene. We tested our approach on the Davis Dynamic Vision Sensor event-based camera.en_US
dc.identifier.urihttps://hdl.handle.net/10133/5692
dc.language.isoen_USen_US
dc.proquest.subject0984en_US
dc.proquestyesYesen_US
dc.publisherLethbridge, Alta. : Universtiy of Lethbridge, Department of Mathematics and Computer Scienceen_US
dc.publisher.departmentDepartment of Mathematics and Computer Scienceen_US
dc.publisher.facultyArts and Scienceen_US
dc.relation.ispartofseriesThesis (University of Lethbridge. Faculty of Arts and Science)en_US
dc.subjectComputer visionen_US
dc.subjectComputer algorithmsen_US
dc.subjectOpticsen_US
dc.subjectDigital imagesen_US
dc.subjectDeconvolutionen_US
dc.subjectDissertations, Academicen_US
dc.titleBio-inspired looming object detection using event-based camerasen_US
dc.typeThesisen_US
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