  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%.
