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  • 1.
    Cheng, Xiaogang
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China; School of Electrical Engineering and Computer Science, Royal Institute of Technology, Stockholm, Sweden.
    Yang, Bin
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. School of Environmental and Municipal Engineering, Xi’an University of Architecture and Technology, Xi'an, China.
    Liu, Guoqing
    Olofsson, Thomas
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Li, Haibo
    A total bounded variation approach to low visibility estimation on expressways2018In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 2, article id 392Article in journal (Refereed)
    Abstract [en]

    Low visibility on expressways caused by heavy fog and haze is a main reason for traffic accidents. Real-time estimation of atmospheric visibility is an effective way to reduce traffic accident rates. With the development of computer technology, estimating atmospheric visibility via computer vision becomes a research focus. However, the estimation accuracy should be enhanced since fog and haze are complex and time-varying. In this paper, a total bounded variation (TBV) approach to estimate low visibility (less than 300 m) is introduced. Surveillance images of fog and haze are processed as blurred images (pseudo-blurred images), while the surveillance images at selected road points on sunny days are handled as clear images, when considering fog and haze as noise superimposed on the clear images. By combining image spectrum and TBV, the features of foggy and hazy images can be extracted. The extraction results are compared with features of images on sunny days. Firstly, the low visibility surveillance images can be filtered out according to spectrum features of foggy and hazy images. For foggy and hazy images with visibility less than 300 m, the high-frequency coefficient ratio of Fourier (discrete cosine) transform is less than 20%, while the low-frequency coefficient ratio is between 100% and 120%. Secondly, the relationship between TBV and real visibility is established based on machine learning and piecewise stationary time series analysis. The established piecewise function can be used for visibility estimation. Finally, the visibility estimation approach proposed is validated based on real surveillance video data. The validation results are compared with the results of image contrast model. Besides, the big video data are collected from the Tongqi expressway, Jiangsu, China. A total of 1,782,000 frames were used and the relative errors of the approach proposed are less than 10%.

  • 2.
    Cheng, Xiaogang
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. Nanjing Univ Posts & Telecommun, Coll Telecommun & Informat Engn, Nanjing, Jiangsu, Peoples R China; Royal Inst Technol, Sch Comp Sci & Commun, Stockholm, Sweden.
    Yang, Bin
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Liu, Guoqing
    Olofsson, Thomas
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Li, Haibo
    A variational approach to atmospheric visibility estimation in the weather of fog and haze2018In: Sustainable cities and society, ISSN 2210-6707, Vol. 39, p. 215-224Article in journal (Refereed)
    Abstract [en]

    Real-time atmospheric visibility estimation in foggy and hazy weather plays a crucial role in ensuring traffic safety. Overcoming the inherent drawbacks with traditional optical estimation methods, like limited sampling volume and high cost, vision-based approaches have received much more attention in recent research on atmospheric visibility estimation. Based on the classical Koschmieder's formula, atmospheric visibility estimation is carried out by extracting an inherent extinction coefficient. In this paper we present a variational framework to handle the nature of time-varying extinction coefficient and develop a novel algorithm of extracting the extinction coefficient through a piecewise functional fitting of observed luminance curves. The developed algorithm is validated and evaluated with a big database of road traffic video from Tongqi expressway (in China). The test results are very encouraging and show that the proposed algorithm could achieve an estimation error rate of 10%. More significantly, it is the first time that the effectiveness of Koschmieder's formula in atmospheric visibility estimation was validated with a big dataset, which contains more than two million luminance curves extracted from real-world traffic video surveillance data.

  • 3.
    Cheng, Xiaogang
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing, China.
    Yang, Bin
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Olofsson, Thomas
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Liu, Guoqing
    Li, Haibo
    A pilot study of online non-invasive measuring technology based on video magnification to determine skin temperature2017In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 121, p. 1-10Article in journal (Refereed)
    Abstract [en]

    Much attention was paid on human centered design strategies for environmental control systems of indoor built environments. The goal is to achieve thermally comfortable, healthy and safe working or living environments in energy efficient manners. Normally building Heating, Ventilation and Air Conditioning (HVAC) systems have fixed operating settings, which can't satisfy human thermal comfort requirements under transient and non-uniform indoor thermal environments. Therefore, human thermal physiology signal such as skin temperature, which can reflect human body thermal sensation, has to be measured over time. Several trials have been performed by minimizing measuring sensors such as i-Button and mounting measuring sensors into wearable devices such as glasses. Infrared thermography technology has also been tried to achieve non-invasive measurements. However, it would be much more convenient and feasible if normal computer camera could record images, which could be used to obtain human thermal physiology signals. In this study, skin temperature of hand back, which has a high density of blood vessels and is normally not covered by clothing, was measured by i-button sensors. Images recorded by normal camera were amplified to analyzing skin temperature variation, which are impossible to see with naked eyes. The agreement between i-button sensor measuring results and image magnification results demonstrated the possibility of non-invasive measuring technology by image magnification. Partly personalized saturation-temperature model (T = 96.5 × S + bi) can be used to predict skin temperatures for young East Asia females.

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