Bio-inspired speed detection and discrimination

TL;DR

This paper introduces a bio-inspired parallel architecture for motion detection in computer vision, offering a wider detection range and avoiding error propagation typical of serial multiscale methods.

Contribution

The work presents a novel parallel scheme inspired by biological motion perception, improving speed detection range and robustness over traditional serial multiscale approaches.

Findings

Parallel architecture achieves similar accuracy to serial methods.

The approach extends detection range without error propagation.

Biologically inspired design enhances robustness in motion detection.

Abstract

In the field of computer vision, a crucial task is the detection of motion (also called optical flow extraction). This operation allows analysis such as 3D reconstruction, feature tracking, time-to-collision and novelty detection among others. Most of the optical flow extraction techniques work within a finite range of speeds. Usually, the range of detection is extended towards higher speeds by combining some multiscale information in a serial architecture. This serial multi-scale approach suffers from the problem of error propagation related to the number of scales used in the algorithm. On the other hand, biological experiments show that human motion perception seems to follow a parallel multiscale scheme. In this work we present a bio-inspired parallel architecture to perform detection of motion, providing a wide range of operation and avoiding error propagation associated with the serial architecture. To test our algorithm, we perform relative error comparisons between both classical and proposed techniques, showing that the parallel architecture is able to achieve motion detection with results similar to the serial approach.