Object Motion Sensitivity: A Bio-inspired Solution to the Ego-motion Problem for Event-based Cameras
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Abstract (arXiv)
Neuromorphic (event-based) image sensors draw inspiration from the human-retina to create an electronic device that can process visual stimuli in a way that closely resembles its biological counterpart. These sensors process information significantly different than the traditional RGB sensors. Specifically, the sensory information generated by event-based image sensors are orders of magnitude sparser compared to that of RGB sensors. The first generation of neuromorphic image sensors, Dynamic Vision Sensor (DVS), are inspired by the computations confined to the photoreceptors and the first retinal synapse. In this work, we highlight the capability of the second generation of neuromorphic image sensors, Integrated Retinal Functionality in CMOS Image Sensors (IRIS), which aims to mimic full retinal computations from photoreceptors to output of the retina (retinal ganglion cells) for targeted feature-extraction. The feature of choice in this work is Object Motion Sensitivity (OMS) that is processed locally in the IRIS sensor. Our results show that OMS can accomplish standard computer vision tasks with similar efficiency to conventional RGB and DVS solutions but offers drastic bandwidth reduction. This cuts the wireless and computing power budgets and opens up vast opportunities in high-speed, robust, energy-efficient, and low-bandwidth real-time decision making.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 17 of its 56 references that are in the map. First posted on arXiv 24 Mar 2023.
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17 of 56 references.
References · 17
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- 2019EVDodgeNet: Deep Dynamic Obstacle Dodging with Event Camerascited by 46
- 2020Globally Optimal Contrast Maximisation for Event-based Motion Estimationcited by 33
- 2022IRIS: Integrated Retinal Functionality in Image Sensorscited by 4
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- 2022Dynamic Registration: Joint Ego Motion Estimation and 3D Moving Object Detection in Dynamic Environmentcited by 1
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