Dataset Information

A Saccade Based Framework for Real-Time Motion Segmentation Using Event Based Vision Sensors.

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%.

SUBMITTER: Mishra A

PROVIDER: S-EPMC5334512 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

A Saccade Based Framework for Real-Time Motion Segmentation Using Event Based Vision Sensors.

Mishra Abhishek A Ghosh Rohan R Principe Jose C JC Thakor Nitish V NV Kukreja Sunil L SL

Frontiers in neuroscience 20170303

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 ...[more]

PMID: 28316563

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Dataset Information

A Saccade Based Framework for Real-Time Motion Segmentation Using Event Based Vision Sensors.

Publications

A Saccade Based Framework for Real-Time Motion Segmentation Using Event Based Vision Sensors.

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