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Adaptive Time-Slice Block-Matching Optical Flow Algorithm for Dynamic Vision Sensors


Liu, Min; Delbruck, T (2018). Adaptive Time-Slice Block-Matching Optical Flow Algorithm for Dynamic Vision Sensors. In: British Machine Vision Conference (BMVC) 2018, Newcastle upon Tyne, UK, 3 September 2018 - 6 September 2018.

Abstract

Dynamic Vision Sensors (DVS) output asynchronous log intensity change events. They have potential applications in high-speed robotics, autonomous cars and drones. The precise event timing, sparse output, and wide dynamic range of the events are well suited for optical flow, but conventional optical flow (OF) algorithms are not well matched to the event stream data. This paper proposes an event-driven OF algorithm called adaptive block-matching optical flow (ABMOF). ABMOF uses time slices of accumulated DVS events. The time slices are adaptively rotated based on the input events and OF results. Compared with other methods such as gradient-based OF, ABMOF can efficiently be implemented in compact logic circuits. We developed both ABMOF and Lucas-Kanade (LK) algorithms using our adapted slices. Results shows that ABMOF accuracy is comparable with LK accuracy on natural scene data including sparse and dense texture, high dynamic range, and fast motion exceeding 30,000 pixels per second.

Abstract

Dynamic Vision Sensors (DVS) output asynchronous log intensity change events. They have potential applications in high-speed robotics, autonomous cars and drones. The precise event timing, sparse output, and wide dynamic range of the events are well suited for optical flow, but conventional optical flow (OF) algorithms are not well matched to the event stream data. This paper proposes an event-driven OF algorithm called adaptive block-matching optical flow (ABMOF). ABMOF uses time slices of accumulated DVS events. The time slices are adaptively rotated based on the input events and OF results. Compared with other methods such as gradient-based OF, ABMOF can efficiently be implemented in compact logic circuits. We developed both ABMOF and Lucas-Kanade (LK) algorithms using our adapted slices. Results shows that ABMOF accuracy is comparable with LK accuracy on natural scene data including sparse and dense texture, high dynamic range, and fast motion exceeding 30,000 pixels per second.

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Additional indexing

Item Type:Conference or Workshop Item (Speech), not_refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:6 September 2018
Deposited On:12 Mar 2019 11:58
Last Modified:12 Mar 2019 11:58
Publisher:Proceedings of BMVC 2018
OA Status:Green
Free access at:Official URL. An embargo period may apply.
Official URL:http://bmvc2018.org/contents/papers/0280.pdf

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