Retina-Inspired Object Motion Segmentation for Event-Cameras
This work addresses the problem of efficient, low-parameter motion segmentation for event-cameras, offering a domain-agnostic solution that is incremental in applying retinal neuroscience to computer vision.
The paper tackles object motion segmentation for event-cameras by developing a bio-inspired algorithm based on Object Motion Sensitivity, which reduces parameters by 10^3 to 10^6 orders of magnitude compared to prior methods.
Event-cameras have emerged as a revolutionary technology with a high temporal resolution that far surpasses standard active pixel cameras. This technology draws biological inspiration from photoreceptors and the initial retinal synapse. This research showcases the potential of additional retinal functionalities to extract visual features. We provide a domain-agnostic and efficient algorithm for ego-motion compensation based on Object Motion Sensitivity (OMS), one of the multiple features computed within the mammalian retina. We develop a method based on experimental neuroscience that translates OMS' biological circuitry to a low-overhead algorithm to suppress camera motion bypassing the need for deep networks and learning. Our system processes event data from dynamic scenes to perform pixel-wise object motion segmentation using a real and synthetic dataset. This paper introduces a bio-inspired computer vision method that dramatically reduces the number of parameters by $\text{10}^\text{3}$ to $\text{10}^\text{6}$ orders of magnitude compared to previous approaches. Our work paves the way for robust, high-speed, and low-bandwidth decision-making for in-sensor computations.