Real-time Tracking Based on Neuromrophic Vision

TL;DR

This paper presents a real-time object tracking method that integrates computer vision algorithms with neuromorphic vision sensors, achieving high-speed tracking at 100 time bins per second, surpassing conventional camera-based methods.

Contribution

It introduces a novel approach combining computer vision tracking with neuromorphic sensor encoding, demonstrating fast real-time tracking capabilities.

Findings

Achieved 100 time bins per second tracking speed.

Successfully integrated vision algorithms with neuromorphic sensors.

Demonstrated potential for faster tracking than conventional cameras.

Abstract

Real-time tracking is an important problem in computer vision in which most methods are based on the conventional cameras. Neuromorphic vision is a concept defined by incorporating neuromorphic vision sensors such as silicon retinas in vision processing system. With the development of the silicon technology, asynchronous event-based silicon retinas that mimic neuro-biological architectures has been developed in recent years. In this work, we combine the vision tracking algorithm of computer vision with the information encoding mechanism of event-based sensors which is inspired from the neural rate coding mechanism. The real-time tracking of single object with the advantage of high speed of 100 time bins per second is successfully realized. Our method demonstrates that the computer vision methods could be used for the neuromorphic vision processing and we can realize fast real-time tracking using neuromorphic vision sensors compare to the conventional camera.