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Combined frame- and event-based detection and tracking


Liu, Hongjie; Moeys, Diederik Paul; Das, Gautham; Neil, Daniel; Liu, Shih-Chii; Delbruck, Tobi (2016). Combined frame- and event-based detection and tracking. In: IEEE International Symposium on Circuits and Systems (ISCAS) 2016, Montreal, Canada, 22 May 2016 - 25 May 2016.

Abstract

This paper reports an object tracking algorithm for a moving platform using the dynamic and active-pixel vision sensor (DAVIS). It takes advantage of both the active pixel sensor (APS) frame and dynamic vision sensor (DVS) event outputs from the DAVIS. The tracking is performed in a three step-manner: regions of interest (ROIs) are generated by a cluster-based tracking using the DVS output, likely target locations are detected by using a convolutional neural network (CNN) on the APS output to classify the ROIs as foreground and background, and finally a particle filter infers the target location from the ROIs. Doing convolution only in the ROIs boosts the speed by a factor of 70 compared with full-frame convolutions for the 240x180 frame input from the DAVIS. The tracking accuracy on a predator and prey robot database reaches 90% with a cost of less than 20ms/frame in Matlab on a normal PC without using a GPU.

Abstract

This paper reports an object tracking algorithm for a moving platform using the dynamic and active-pixel vision sensor (DAVIS). It takes advantage of both the active pixel sensor (APS) frame and dynamic vision sensor (DVS) event outputs from the DAVIS. The tracking is performed in a three step-manner: regions of interest (ROIs) are generated by a cluster-based tracking using the DVS output, likely target locations are detected by using a convolutional neural network (CNN) on the APS output to classify the ROIs as foreground and background, and finally a particle filter infers the target location from the ROIs. Doing convolution only in the ROIs boosts the speed by a factor of 70 compared with full-frame convolutions for the 240x180 frame input from the DAVIS. The tracking accuracy on a predator and prey robot database reaches 90% with a cost of less than 20ms/frame in Matlab on a normal PC without using a GPU.

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Additional indexing

Item Type:Conference or Workshop Item (Speech), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Language:English
Event End Date:25 May 2016
Deposited On:27 Jan 2017 08:34
Last Modified:09 Sep 2017 23:42
Publisher:Proceedings of 2016 IEEE International Symposium on Circuits and Systems (ISCAS)
Series Name:IEEE Int. Symposium on Circuits and Systems (ISCAS)
Publisher DOI:https://doi.org/10.1109/ISCAS.2016.7539103

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