AFOD An Adaptable Framework for Object in Event-based Vision.pdf (1.4 MB)
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AFOD: An Adaptable Framework for Object Detection in Event-based Vision

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posted on 2021-07-10, 03:15 authored by Shixiong ZhangShixiong Zhang, Wenmin WangWenmin Wang
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.


Science and Technology Development Fund (FDCT) of Macau (0016/2019/A1)


Email Address of Submitting Author

Submitting Author's Institution

International Institute of Next Generation Internet, Macau University of Science and Technology, Avenida Wai Long, Taipa, Macau,

Submitting Author's Country

  • China

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