{"id":{"repo_id":"lethbridge","oai_identifier":"oai:opus.uleth.ca:10133/5015"},"canonical_url":"https://search.dev.ndltd.org/etd/lethbridge/oai:opus.uleth.ca:10133/5015","repository":{"repo_id":"lethbridge","name":"University of Lethbridge","base_url":"https://opus.uleth.ca/server/oai/request"},"display":{"title":"Looming object detection with event-based cameras","abstract":"We present a looming object detection method for event-based cameras. Event-based cameras detect events asynchronously which eliminates the unnecessary computation required for the conventional frame-based cameras. There are two main parts of this method. In the first part, we develop an event-based optical flow algorithm. The algorithm is based on Reichardt motion detectors inspired by the fly visual system and has a very low computational requirement for each event received from the event-based camera. In the second part, we develop an algorithm to detect looming objects using the output from the first algorithm. This proposed method is only sensitive to significant log-luminance changes, which results in low energy consumption. We have performed several experiments with our method using the Davis Dynamic Vision Sensor (DVS) which is an event-based camera. Experimental results show that our event-based looming detection algorithm accurately detects looming objects in all cases when there is a single object moving in the scene. It also does not report looming when no objects are looming. Our algorithm is fast and operates in real-time, requiring only microseconds to process each event.","abstract_html":"We present a looming object detection method for event-based cameras. Event-based cameras detect events asynchronously which eliminates the unnecessary computation required for the conventional frame-based cameras. There are two main parts of this method. In the first part, we develop an event-based optical flow algorithm. The algorithm is based on Reichardt motion detectors inspired by the fly visual system and has a very low computational requirement for each event received from the event-based camera. In the second part, we develop an algorithm to detect looming objects using the output from the first algorithm. This proposed method is only sensitive to significant log-luminance changes, which results in low energy consumption. We have performed several experiments with our method using the Davis Dynamic Vision Sensor (DVS) which is an event-based camera. Experimental results show that our event-based looming detection algorithm accurately detects looming objects in all cases when there is a single object moving in the scene. It also does not report looming when no objects are looming. Our algorithm is fast and operates in real-time, requiring only microseconds to process each event.","abstract_has_math":false,"creators":["Ridwan, Iffatur"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017","date_published":"2017","updated_at":"2026-07-27T20:02:05Z","subjects":["Communication and technology","Computer systems","event-based cameras","looming object detection"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:10133/5015"],"render_values":[{"text":"hdl:10133/5015","href":null,"code":true}]}]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2017"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Communication and technology","Computer systems","event-based cameras","looming object detection"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["hdl:10133/5015"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.other","label":"Dc Description Other","values":["We present a looming object detection method for event-based cameras. Event-based cameras detect events asynchronously which eliminates the unnecessary computation required for the conventional frame-based cameras. There are two main parts of this method. In the first part, we develop an event-based optical flow algorithm. The algorithm is based on Reichardt motion detectors inspired by the fly visual system and has a very low computational requirement for each event received from the event-based camera. In the second part, we develop an algorithm to detect looming objects using the output from the first algorithm. This proposed method is only sensitive to significant log-luminance changes, which results in low energy consumption. We have performed several experiments with our method using the Davis Dynamic Vision Sensor (DVS) which is an event-based camera. Experimental results show that our event-based looming detection algorithm accurately detects looming objects in all cases when there is a single object moving in the scene. It also does not report looming when no objects are looming. Our algorithm is fast and operates in real-time, requiring only microseconds to process each event."]},{"key":"dc:title","label":"Title","values":["Looming object detection with event-based cameras"]}]}],"canonical_facts":{"dc:date.issued":["2017"],"dc:description.other":["We present a looming object detection method for event-based cameras. Event-based cameras detect events asynchronously which eliminates the unnecessary computation required for the conventional frame-based cameras. There are two main parts of this method. In the first part, we develop an event-based optical flow algorithm. The algorithm is based on Reichardt motion detectors inspired by the fly visual system and has a very low computational requirement for each event received from the event-based camera. In the second part, we develop an algorithm to detect looming objects using the output from the first algorithm. This proposed method is only sensitive to significant log-luminance changes, which results in low energy consumption. We have performed several experiments with our method using the Davis Dynamic Vision Sensor (DVS) which is an event-based camera. Experimental results show that our event-based looming detection algorithm accurately detects looming objects in all cases when there is a single object moving in the scene. It also does not report looming when no objects are looming. Our algorithm is fast and operates in real-time, requiring only microseconds to process each event."],"dc:identifier":["hdl:10133/5015"],"dc:subject":["Communication and technology","Computer systems","event-based cameras","looming object detection"],"dc:title":["Looming object detection with event-based cameras"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T20:02:05Z"}