| 1. |
- Chen, Guang, et al.
(författare)
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A Novel Visible Light Positioning System With Event-Based Neuromorphic Vision Sensor
- 2020
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Ingår i: IEEE Sensors Journal. - : Institute of Electrical and Electronics Engineers (IEEE). - 1530-437X .- 1558-1748. ; 20:17, s. 10211-10219
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Tidskriftsartikel (refereegranskat)abstract
- With the advanced development of image processing technology, visible light positioning (VLP) system based on image sensors has attracted more and more attention. However, as a commonly used light receiver, traditional CMOS camera has limited dynamic range and high latency, which is susceptible to various lighting and environmental factors. Moreover, high computational cost from image processing is unavoidable for most of visible light positioning systems. In our work, a novel VLP system using an event-based neuromorphic vision sensor (event camera) as the light receiver is proposed. Due to the low latency and microsecond-level temporal resolution of the event camera, our VLP system is able to identify multiple high-frequency flickering LEDs in asynchronous events simultaneously leaving out the need for data association and traditional image processing methods. A multi-LED fusion method is applied and a high positioning accuracy of 3cm is achieved when the height between LEDs and the event camera is within 1m.
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| 2. |
- Chen, Guang, et al.
(författare)
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EDDD : Event-Based Drowsiness Driving Detection Through Facial Motion Analysis With Neuromorphic Vision Sensor
- 2020
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Ingår i: IEEE Sensors Journal. - : IEEE. - 1530-437X .- 1558-1748. ; 20:11, s. 6170-6181
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Tidskriftsartikel (refereegranskat)abstract
- Drowsiness driving is a principal factor of many fatal traffic accidents. This paper presents the first event-based drowsiness driving detection (EDDD) system by using the recently developed neuromorphic vision sensor. Compared with traditional frame-based cameras, neuromorphic vision sensors, such as Dynamic Vision Sensors (DVS), have a high dynamic range and do not acquire full images at a fixed frame rate but rather have independent pixels that output intensity changes (called events) asynchronously at the time they occur. Since events are generated by moving edges in the scene, DVS is considered as an efficient and effective detector for the drowsiness driving-related motions. Based on this unique output, this work first proposes a highly efficient method to recognize and localize the driver's eyes and mouth motions from event streams. We further design and extract event-based drowsiness-related features directly from the event streams caused by eyes and mouths motions, then the EDDD model is established based on these features. Additionally, we provide the EDDD dataset, the first public dataset dedicated to event-based drowsiness driving detection. The EDDD dataset has 260 recordings in daytime and evening with several challenging scenes such as subjects wearing glasses/sunglasses. Experiments are conducted based on this dataset and demonstrate the high efficiency and accuracy of our method under different illumination conditions. As the first investigation of the usage of DVS in drowsiness driving detection applications, we hope that this work will inspire more event-based drowsiness driving detection research.
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