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NeuroIV :
NeuroIV : Neuromorphic Vision Meets Intelligent Vehicle Towards Safe Driving With a New Database and Baseline Evaluations
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- Chen, Guang (author)
- Tongji Univ, Sch Automot Studies, Shanghai 200092, Peoples R China.;State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410012, Peoples R China.;Tech Univ Munich, Dept Informat, D-80333 Munich, Germany.
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- Wang, Fa (author)
- Tongji Univ, Sch Automot Studies, Shanghai 200092, Peoples R China.
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- Li, Weijun (author)
- Tongji Univ, Sch Automot Studies, Shanghai 200092, Peoples R China.
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- Hong, Lin (author)
- Shandong Univ Sci & Technol, Sch Transportat, Qingdao 266510, Peoples R China.
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- Conradt, Jörg (author)
- KTH,Beräkningsvetenskap och beräkningsteknik (CST)
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- Chen, Jieneng (author)
- Tongji Univ, Sch Automot Studies, Shanghai 200092, Peoples R China.
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- Zhang, Zhenyan (author)
- Tongji Univ, Sch Automot Studies, Shanghai 200092, Peoples R China.
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- Lu, Yiwen (author)
- Tongji Univ, Sch Automot Studies, Shanghai 200092, Peoples R China.
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- Knoll, Alois (author)
- Tech Univ Munich, Dept Informat, D-80333 Munich, Germany.
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Tongji Univ, Sch Automot Studies, Shanghai 200092, Peoples R China;State Key Lab Adv Design & Mfg Vehicle Body, Changsha 410012, Peoples R China.;Tech Univ Munich, Dept Informat, D-80333 Munich, Germany. Tongji Univ, Sch Automot Studies, Shanghai 200092, Peoples R China. (creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2022
- 2022
- English.
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In: IEEE transactions on intelligent transportation systems (Print). - : Institute of Electrical and Electronics Engineers (IEEE). - 1524-9050 .- 1558-0016. ; 23:2, s. 1171-1183
- Related links:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- 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 low-latency 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 and adapt state-of-the-art convolutional architectures to extensively evaluate their performances on this dataset. Together with qualitative and quantitative results, this work provides a new database and baseline evaluations named NeuroIV in cross-cutting areas of neuromorphic vision and intelligent vehicle.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Reglerteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Control Engineering (hsv//eng)
Keyword
- Neuromorphics
- Vision sensors
- Intelligent sensors
- Intelligent vehicles
- Cameras
- Neuromorphic vision
- distracted driving
- advanced driver assistance system
- database and baseline evaluations
- event encoding
- deep learning
Publication and Content Type
- ref (subject category)
- art (subject category)
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