Block-Matching Optical Flow for Dynamic Vision Sensor- Algorithm and FPGA Implementation

TL;DR

This paper introduces a novel block-matching optical flow algorithm for dynamic vision sensors, implemented in FPGA, achieving faster processing and improved accuracy over previous methods, suitable for robotics applications.

Contribution

A new event-based optical flow algorithm inspired by MPEG motion estimation, implemented in FPGA for high-speed, low-power processing, and demonstrating improved accuracy.

Findings

30% reduction in average angular error

FPGA implementation runs 20 times faster than software

Effective on scenes with edges, features, and textures

Abstract

Rapid and low power computation of optical flow (OF) is potentially useful in robotics. The dynamic vision sensor (DVS) event camera produces quick and sparse output, and has high dynamic range, but conventional OF algorithms are frame-based and cannot be directly used with event-based cameras. Previous DVS OF methods do not work well with dense textured input and are designed for implementation in logic circuits. This paper proposes a new block-matching based DVS OF algorithm which is inspired by motion estimation methods used for MPEG video compression. The algorithm was implemented both in software and on FPGA. For each event, it computes the motion direction as one of 9 directions. The speed of the motion is set by the sample interval. Results show that the Average Angular Error can be improved by 30\% compared with previous methods. The OF can be calculated on FPGA with 50\,MHz clock in 0.2\,us per event (11 clock cycles), 20 times faster than a Java software implementation running on a desktop PC. Sample data is shown that the method works on scenes dominated by edges, sparse features, and dense texture.