摘要：
Fatigue driving is a serious threat to road safety, which is why accurately identifying fatigue driving behavior and warning drivers in time are of great significance in improving traffic safety. However, accurately recognizing fatigue driving is still challenging due to large intra-class variations in facial expression, continuity of behaviors, and illumination conditions. A fatigue driving recognition method based on feature parameter images and a residual Swin Transformer is proposed in this paper. First, the face region is detected through spatial pyramid pooling and a multi-scale feature output module. Then, a multi-scale facial landmark detector is used to locate 23 key points on the face. The aspect ratios of the eyes and mouth are calculated based on the coordinates of these key points, and a feature parameter matrix for fatigue driving recognition is obtained. Finally, the feature parameter matrix is converted into an image, and the residual Swin Transformer network is presented to recognize fatigue driving. Experimental results on the HNUFD dataset show that the proposed method achieves an accuracy of 96.512%, thus outperforming state-of-the-art methods.
通讯机构：
[Hongli Liu] C;College of Electrical and Information Engineering, Hunan University, Changsha 410082, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
deep learning;facial landmark detector;fatigue driving recognition;multi-scale
摘要：
<jats:p>Fatigue driving behavior recognition in all-weather real driving environments is a challenging task. Accurate recognition of fatigue driving behavior is helpful to improve traffic safety. The facial landmark detector is crucial to fatigue driving recognition. However, existing facial landmark detectors are mainly aimed at stable front face color images instead of side face gray images, which is difficult to adapt to the fatigue driving behavior recognition in real dynamic scenes. To maximize the driver’s facial feature information and temporal characteristics, a fatigue driving behavior recognition method based on a multi-scale facial landmark detector (MSFLD) is proposed. First, a spatial pyramid pooling and multi-scale feature output (SPP-MSFO) detection model is built to obtain a face region image. The MSFLD is a lightweight facial landmark detector, which is composed of convolution layers, inverted bottleneck blocks, and multi-scale full connection layers to achieve accurate detection of 23 key points on the face. Second, the aspect ratios of the left eye, right eye and mouth are calculated in accordance with the coordinates of the key points to form a fatigue parameter matrix. Finally, the combination of adaptive threshold and statistical threshold is used to avoid misjudgment of fatigue driving recognition. The adaptive threshold is dynamic, which solves the problem of the difference in the aspect ratio of the eyes and mouths of different drivers. The statistical threshold is a supplement to solve the problem of driver’s low eye threshold and high mouth threshold. The proposed methods are evaluated on the Hunan University Fatigue Detection (HNUFDD) dataset. The proposed MSFLD achieves a normalized mean error value of 5.4518%, and the accuracy of the fatigue driving recognition method based on MSFLD achieves 99.1329%, which outperforms that of state-of-the-art methods.</jats:p>
期刊：
IEEE INTERNET OF THINGS JOURNAL,2019年6(1):718-733 ISSN：2327-4662
通讯作者：
Qin, Hua
作者机构：
[Qin, Hua; Cao, Buwen; Zeng, Min; Chen, Weihong] Hunan City Univ, Coll Informat & Elect Engn, Yiyang 413000, Peoples R China.;[Li, Jessica; Peng, Yang] Univ Washington, Div Comp & Software Syst, Bothell, WA 98011 USA.
通讯机构：
[Qin, Hua] H;Hunan City Univ, Coll Informat & Elect Engn, Yiyang 413000, Peoples R China.
关键词：
Dual radio;energy efficiency;Internet of Things (IoT);pipeline
摘要：
The future Internet of Things (IoT) will enable Internet connectivity for a vast amount of battery-powered devices, which usually need to communicate with each other or to some remote gateways through multihop communications. Although ZigBee has become a widely used communication technology in IoT, Wi-Fi, on the other hand, has its unique advantages such as high throughput and native IP compatibility, despite its potentially higher energy consumption. With the development of IoT, more and more IoT devices are equipped with multiple radio interfaces, such as both Wi-Fi and ZigBee. Inspired by this, we propose a dual-interface dual-pipeline scheduling (DIPS) scheme, which leverages an activation pipeline mainly constructed by low-power ZigBee interfaces to wake up a data pipeline constructed by high-power Wi-Fi interfaces on demand, toward enabling multihop data delivery in IoT. The objective is to minimize network energy consumption while satisfying certain end-to-end delay requirements. Extensive simulations and prototype-based experiments have been conducted. The results show that the energy consumption of DIPS is 96.5% and 92.8% lower than that of the IEEE 802.11's standard power saving scheme and a state-of-the-art pipeline-based scheme in moderate traffic scenarios, respectively.
关键词：
Deep learning;Fuzzy representation;Residual networks;Traffic flow prediction
摘要：
Predicting traffic flow is one of the fundamental needs to comfortable travel, but this task is challenging in vehicular cyber-physical systems because of ever-increasing uncertain traffic big data. Although deep learning (DL) methods with outstanding performance recently have become popular, most existing DL models for traffic flow prediction are fully deterministic and shed no light on data uncertainty. In this study, a novel fuzzy deep-learning approach called FDCN is proposed for predicting citywide traffic flow. This approach is built on the fuzzy theory and the deep residual network model. Our key idea is to introduce the fuzzy representation into the DL model to lessen the impact of data uncertainty. A model of fuzzy deep convolutional network is established to improve traffic flow prediction while investigating the spatial and temporal correlation of traffic flow. We further propose pre-training and fine-tuning strategies that efficiently learn parameters of the FDCN. To the best of our knowledge, this is the first time that a fuzzy DL approach has been applied to represent traffic features for traffic flow prediction. Experimental results demonstrate that the proposed approach to traffic flow prediction has superior performance compared with state-of-the-art approaches. (C) 2018 Elsevier B.V. All rights reserved.
期刊：
International Journal of Applied Mathematics & Statistics,2013年51(21):484-493 ISSN：0973-1377
作者机构：
[Xiao, Weichu] College of Communication and Electronic Engineering, Hunan City University, Yiyang 413002, Hunan, China;[Chen, Weihong] College of Information Science and Engineering, Hunan City University, Yiyang 413002 Hunan, China
关键词：
Benchmark information diffusion;LBP model;Remote sensing image;Support vector machine
期刊：
International Journal of Applied Mathematics & Statistics,2013年51(22):333-342 ISSN：0973-1377
作者机构：
[Xiao, Weichu] College of Communication and Electronic Engineering, Hunan City University, Yiyang 413002, Hunan, China;[Chen, Weihong] College of Information Science and Engineering, Hunan City University, Yiyang 413002 Hunan, China
关键词：
Adaptive color image;Brightness;Color threshold;Contrast;Image segmentation
期刊：
International Journal of Applied Mathematics & Statistics,2013年45(15):404-412 ISSN：0973-1377
通讯作者：
Xiao, W.
作者机构：
[Xiao, Weichu] School of Communication and Electronic Engineering, Hunan City University, Yiyang 413002, Hunan, China;[Chen, Weihong] School of Information Science and Engineering, Hunan City University, Yiyang 413002, Hunan, China
通讯机构：
[Xiao, W.] S;School of Communication and Electronic Engineering, , Yiyang 413002, Hunan, China
关键词：
Image segmentation;Level set method;Local image information;Shape apriori
期刊：
Journal of Theoretical and Applied Information Technology,2012年44(2):271-277 ISSN：1817-3195
通讯作者：
Xiao, W.
作者机构：
[Chen, Weihong] School of Communication and Electronic Engineering, Hunan City University, Yiyang 413002, Hunan, China;[Guo, Baolong] Institute of Intelligent Control and Image Engineering, Xidian University, Xi'an 710071, Shanxi, China;[Xiao, Weichu] School of Communication and Electronic Engineering, Hunan City University, Yiyang 413002, Hunan, China<&wdkj&>Institute of Intelligent Control and Image Engineering, Xidian University, Xi'an 710071, Shanxi, China
通讯机构：
School of Communication and Electronic Engineering, Hunan City University, China
期刊：
International Review on Computers and Software,2012年7(4):1931-1937 ISSN：1828-6003
通讯作者：
Xiao, W.
作者机构：
[Chen, Weihong] School of Communication and Electronic Engineering, Hunan City University, Yiyang 413002, Hunan, China;[Guo, Baolong] Institute of Intelligent Control and Image Engineering, Xidian University, Xi'an 710071, Shanxi, China;[Xiao, Weichu] School of Communication and Electronic Engineering, Hunan City University, Yiyang 413002, Hunan, China<&wdkj&>Institute of Intelligent Control and Image Engineering, Xidian University, Xi'an 710071, Shanxi, China
通讯机构：
School of Communication and Electronic Engineering, Hunan City University, China
期刊：
International Review on Computers and Software,2011年6(6):1163-1168 ISSN：1828-6003
通讯作者：
Xiao, W.
作者机构：
[Xiao, Weichu] School of Communication and Electronic Engineering, Hunan City University, Yiyang 413000, Hunan, China;[Chen, Weihong; Fei, Xiongwei] School of Information Science and Engineering, Hunan City University, Yiyang 413000, Hunan, China
通讯机构：
School of Communication and Electronic Engineering, Hunan City University, China
关键词：
Collaborative;Collaborative tracking;Distance algorithm;Distributed architecture;Multi-camera surveillance;Multi-cameras;Path models;Quality of services (QoS);Target shape;Algorithms;Data fusion;Quality of service;Security systems;Surface discharges;Video cameras;Cameras
