Comparison and Analysis of Object Detection Algorithm Based on Dynamic Vision Sensor
QIU Zhongyu， ZHAO Wenlong， GAO Wen， PAN Hongtao， SHI Randong
Shanghai Electro-Mechanical Engineering Institute， Shanghai 201109， China
Abstract Dynamic vision sensors, with microsecond-level time resolution and low-delay characteristic, have a great application value in the challenging scenarios with high speed and high dynamic range. In order to compare the differences of the different detection algorithms in event detection, the integral model and Leaky Surface model are used to handle the output events. In addition, two kinds of event-based object detection algorithms are listed, that is, event-based feature detection algorithm and event-based convolutional neural networks (CNN) detection algorithm. By means of object detection for MINST-DVS and POKER-DVS event dataset, the detection accuracy of the two algorithms is compared, and the advantage of event-based deep learning detection algorithm in multi-target and high-speed scenarios is verified .
Key words ：
dynamic vision sensor
event
object detection
CNN
feature detection
Received: 27 September 2021
Published: 24 December 2021
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2021, Vol. 4 
