Liu, Hongjie, Moeys, Diederik Paul, Das, Gautham , Neil, Daniel, Liu, Shih-Chii and Delbruck, Tobi (2016) Combined frame- and event-based detection and tracking. In: 2016 IEEE International Symposium on Circuits and Systems (ISCAS), Montreal, QC, Canada.
Full content URL: https://doi.org/10.1109/ISCAS.2016.7539103
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| Item Type: | Conference or Workshop contribution (Presentation) |
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| Item Status: | Live Archive |
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.
| Keywords: | Event-based tracking and detection, DVS, DAVIS, particle filtering, Convolutional Neural Networks |
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| Subjects: | G Mathematical and Computer Sciences > G740 Computer Vision |
| Divisions: | College of Science > Lincoln Institute for Agri-Food Technology |
| ID Code: | 40825 |
| Deposited On: | 30 Sep 2020 10:40 |
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