Please use this identifier to cite or link to this item: https://doi.org/10.3389/fnins.2017.00083
Title: A saccade based framework for real-time motion segmentation using event based vision sensors
Authors: Mishra, A 
Ghosh, R 
Principe, J.C
Thakor, N.V 
Kukreja, S.L 
Keywords: algorithm
Article
biosensor
calculation
camera
controlled study
dynamics
measurement accuracy
methodology
motion
motion segmentation
saccadic eye movement
spatial statistics
statistics
vision
vision sensor
Issue Date: 2017
Citation: Mishra, A, Ghosh, R, Principe, J.C, Thakor, N.V, Kukreja, S.L (2017). A saccade based framework for real-time motion segmentation using event based vision sensors. Frontiers in Neuroscience 11 (MAR) : 83. ScholarBank@NUS Repository. https://doi.org/10.3389/fnins.2017.00083
Abstract: Motion segmentation is a critical pre-processing step for autonomous robotic systems to facilitate tracking of moving objects in cluttered environments. Event based sensors are low power analog devices that represent a scene by means of asynchronous information updates of only the dynamic details at high temporal resolution and, hence, require significantly less calculations. However, motion segmentation using spatiotemporal data is a challenging task due to data asynchrony. Prior approaches for object tracking using neuromorphic sensors perform well while the sensor is static or a known model of the object to be followed is available. To address these limitations, in this paper we develop a technique for generalized motion segmentation based on spatial statistics across time frames. First, we create micromotion on the platform to facilitate the separation of static and dynamic elements of a scene, inspired by human saccadic eye movements. Second, we introduce the concept of spike-groups as a methodology to partition spatio-temporal event groups, which facilitates computation of scene statistics and characterize objects in it. Experimental results show that our algorithm is able to classify dynamic objects with a moving camera with maximum accuracy of 92%. © 2017 Mishra, Ghosh, Principe, Thakor and Kukreja.
Source Title: Frontiers in Neuroscience
URI: https://scholarbank.nus.edu.sg/handle/10635/176098
ISSN: 1662-4548
DOI: 10.3389/fnins.2017.00083
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