Moving Object Detection for Event-based vision using Graph Spectral Clustering

TL;DR

This paper introduces an unsupervised graph spectral clustering method for detecting moving objects in event-based vision data, effectively handling noise and low resolution issues inherent in neuromorphic sensors.

Contribution

The paper presents a novel GSCEventMOD algorithm that automatically determines the number of moving objects and outperforms existing methods on public datasets.

Findings

Outperforms state-of-the-art techniques by up to 30%

Automatically determines the number of moving objects

Effective in noisy, low-resolution event-based data

Abstract

Moving object detection has been a central topic of discussion in computer vision for its wide range of applications like in self-driving cars, video surveillance, security, and enforcement. Neuromorphic Vision Sensors (NVS) are bio-inspired sensors that mimic the working of the human eye. Unlike conventional frame-based cameras, these sensors capture a stream of asynchronous 'events' that pose multiple advantages over the former, like high dynamic range, low latency, low power consumption, and reduced motion blur. However, these advantages come at a high cost, as the event camera data typically contains more noise and has low resolution. Moreover, as event-based cameras can only capture the relative changes in brightness of a scene, event data do not contain usual visual information (like texture and color) as available in video data from normal cameras. So, moving object detection in event-based cameras becomes an extremely challenging task. In this paper, we present an unsupervised Graph Spectral Clustering technique for Moving Object Detection in Event-based data (GSCEventMOD). We additionally show how the optimum number of moving objects can be automatically determined. Experimental comparisons on publicly available datasets show that the proposed GSCEventMOD algorithm outperforms a number of state-of-the-art techniques by a maximum margin of 30%.