Distance Surface for Event-Based Optical Flow
TL
TL;DR
The proposed DistSurf-OF accurately estimates the angle and speed of each events and introduces a notion of “distance surface”—the distance transform computed from the detected events—as a proxy for object texture in absence of the intensity value at each pixel location.
Abstract
We propose DistSurf-OF, a novel optical flow method for neuromorphic cameras. Neuromorphic cameras (or event detection cameras) are an emerging sensor modality that makes use of dynamic vision sensors (DVS) to report asynchronously the log-intensity changes (called “events”) exceeding a predefined threshold at each pixel. In absence of the intensity value at each pixel location, we introduce a notion of “distance surface”—the distance transform computed from the detected events—as a proxy for object texture. The distance surface is then used as an input to the intensity-based optical flow methods to recover the two dimensional pixel motion. Real sensor experiments verify that the proposed DistSurf-OF accurately estimates the angle and speed of each events.
References64 items
1
Inceptive Event Time-Surfaces for Object Classification Using Neuromorphic Cameras
2
Fast Convolutional Distance Transform
3
Focus Is All You Need: Loss Functions for Event-Based Vision
4
Event-Based Motion Segmentation by Motion Compensation
5