Abstract

Event cameras are novel bio-inspired sensors, whose pixels work independently and asynchronously output intensity changes as events. Since the microsecond resolution (no motion blur) and high dynamic range (compatible with extreme light) of events, it is promising to directly segment objects from the sparse and asynchronous event stream in many applications. However, different from the rich cues in video object segmentation, it is challenging to segment complete objects from the sparse event stream. Given the object mask at the initial time, our task aims to segment complete object at any subsequent time in event streams. In this paper, we present the first recurrent framework for continuous-time object segmentation from event stream. To train and test ourt framework, we build a real-world event-based object segmentation dataset that contains event streams, APS images, and object annotations. Extensive experiments on our datasets demonstrate the effectiveness of the proposed recurrent architecture.