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NeuroIV: Neuromorphic Vision Meets Intelligent Vehicle Towards Safe Driving With a New Database and Baseline Evaluations by Guang Chen; Fa Wang; Weijun Li; Lin Hong; Jorg Conradt; Jieneng Chen; Zhenyan Zhang; Yiwen Lu; Alois Knoll is a Engineering article available to read on EtoBox.

What is NeuroIV: Neuromorphic Vision Meets Intelligent Vehicle Towards Safe Driving With a New Database and Baseline Evaluations about?

Neuromorphic vision sensors such as the Dynamic and Active-pixel Vision Sensor (DAVIS) using silicon retina are inspired by biological vision, they generate streams of asynchronous events to indicate local log-intensity brightness changes. Their properties of high temporal resolution, low-bandwidth, lightweight computation, and lowlatency make them a good fit for many applications of motion perception in the intelligent vehicle. However, as a younger and smaller research field compared to classical computer vision, neuromorphic vision is rarely connected with the intelligent vehicle. For this purpose, we present three novel datasets recorded with DAVIS sensors and depth sensor for the distracted driving research and focus on driver drowsiness detection, driver gaze-zone recognition, and driver hand-gesture recognition. To facilitate the comparison with classical computer vision, we record the RGB, depth and infrared data with a depth sensor simultaneously. The total volume of this dataset has 27360 samples. To unlock the potential of neuromorphic vision on the intelligent vehicle, we utilize three popular event-encoding methods to convert asynchronous event slices to event-frames a

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Author

Guang Chen; Fa Wang; Weijun Li; Lin Hong; Jorg Conradt; Jieneng Chen; Zhenyan Zhang; Yiwen Lu; Alois Knoll

Publisher

IEEE; Institute of Electrical and Electronics Engineers; Institute of Electrical and Electronics Engineers (IEEE) (ISSN 1524-9050)

Published

2022

Language

EN

Field

Engineering (Physical Sciences)