ArticleFrontiers in neuroscience2017
A Saccade Based Framework for Real-Time Motion Segmentation Using Event Based Vision Sensors.
Article in Frontiers in neuroscience, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- A Spatial-Motion-Segmentation Algorithm by Fusing EDPA and Motion Compensation.Sensors (Basel, Switzerland) · 2022Article
- Approaching Retinal Ganglion Cell Modeling and FPGA Implementation for Robotics.Entropy (Basel, Switzerland) · 2018Article
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5 authors.
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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
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