Abstract
Event-based vision is a novel bio-inspired vision that has attracted the
interest of many researchers. As a neuromorphic vision, the sensor is
different from the traditional frame-based cameras. It has such
advantages that conventional frame-based cameras can’t match, e.g., high
temporal resolution, high dynamic range(HDR), sparse and minimal motion
blur. Recently, a lot of computer vision approaches have been proposed
with demonstrated success. However, there is a lack of some general
methods to expand the scope of the application of event-based vision. To
be able to effectively bridge the gap between conventional computer
vision and event-based vision, in this paper, we propose an adaptable
framework for object detection in event-based vision.