{"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. 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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: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:contributor.supervisor":["Cheng, Howard"],"dc:creator":["Ridwan, Iffatur"],"dc:date.accessioned":["2018-01-23T17:10:33Z"],"dc:date.available":["2018-01-23T17:10:33Z"],"dc:date.issued":["2017"],"dc:description.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. 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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. 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