Research on library lighting intelligent control based on infrared image processing techniques

Liu, Yuanyuan

doi:10.1016/j.ijleo.2015.03.034

Cited by 6 publications

(4 citation statements)

References 2 publications

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“…Resolution of Thermal Images [64] 60 × 80 (0.005 MP) [65,66] 160 × 120 (0.019 MP) [67][68][69][70][71][72][73][74][75][76][77][78][79][80] 320 × 240 (0.077 MP) [63,81,82] 320 × 256 (0.085 MP) [83,84] 336 × 256 (0.086 MP) [85,86] 384 × 288 (0.111 MP) [59][60][61][62][87][88][89][90][91][92][93][94] 640 × 480 (0.307 MP) [95,96] 640 × 512 (0.328 MP)…”

Section: Referencementioning

confidence: 99%

“…Fleuret et al consider this method to be of reasonable quality to determine the background image [100]. A binary image can also be obtained in the case of setting a threshold in the form of a condition instead of a specific value [74]. D'huys et al used triangular thresholding to binarize images, where for each histogram point between the peak and the end value, the distance to the line passing through the peak and the end point were calculated, and where the threshold was determined as the position at which the distance between the histogram points and the line is maximum [95].…”

Section: Irt Image Segmentationmentioning

confidence: 99%

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Application of the Machine Vision Technology and Infrared Thermography to the Detection of Hoof Diseases in Dairy Cows: A Review

et al. 2021

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Infrared thermography (IRT) is a noninvasive and safe method of displaying the temperature map of objects that can be used to detect hoof diseases and lameness to reduce significant financial costs and physically stress animals. A qualitative bibliometric method based on the analysis of publications by the authors themselves using sophisticated tools of scientific databases was applied in this work. This review presents the fundamentals of IRT as well as recent developments in IRT detection in dairy science, including preprocessing, segmentation, and classification of objects in IRT images. In addition, recent studies dealing with the detection of hoof diseases and lameness using IRT are reviewed. As a result of this study, select previous studies are confronted in terms of technical aspects of IRT measurements such as emissivity, distance, temperature range, and reflected air temperature. Subsequently, recommendations for future IRT measurements are discussed.

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Section: Referencementioning

confidence: 99%

Section: Irt Image Segmentationmentioning

confidence: 99%

Application of the Machine Vision Technology and Infrared Thermography to the Detection of Hoof Diseases in Dairy Cows: A Review

et al. 2021

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“…Although the accuracy is often good -up to 90-95% -but there are significant privacy concerns when it is used to track occupants. Furthermore, the method based on video image analysis could not detect occupants behind obstacles like desks or workstation partitions (Liu, 2015). Recent development in lowpower Bluetooth technology offers lower-cost indoor location detection with room-level accuracy.…”

Section: Occupancy Detection Technologiesmentioning

confidence: 99%

Understanding occupancy and user behaviour through Wi-Fi-based indoor positioning

Wang

Shao

2017

Building Research & Information

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A 30-day monitoring campaign was conducted in a university library building to investigate the usefulness of a novel Wi-Fi based indoor location system for revealing indoor occupancy patterns and related user behaviour. The system has demonstrated its effectiveness in providing occupancy information with a relatively high degree of granularity and accuracy in this study. The occupancy results revealed that the 24-hour opening policy for the library during the term time was not necessary. On the other hand, the 8-hour library-opening duration during the summer vacation could be extended to include the early evening hours to benefit user productivity. Four occupancy patterns were identified based on cluster analysis. Most users were found to belong to the short-occupancy one-time visitor type, while a minority were the long-occupancy users. The cross-correlations between various occupancy parameters were investigated. For example, the pattern of user arrival times at the library was found to be significantly correlated with their study durations. Further data analysis showed that the majority of long-occupancy users tended not to have frequent breaks with some taking no break for 4 hours. This could have implications for their health and wellbeing as well as their productivity.

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“…Image analysis based on visible and infrared videos have been increasingly used to detect the indoor occupancy with a reasonable 5 accuracy, but there is a great concern about the privacy issues. Although the infrared version of the image analysis based method is more privacy friendly, it could not detect occupants behind obstacles like desk screens and bookshelves [23]. Bluetooth positioning technology is accurate and low cost and has lower power consumption but many devices carried around by building users do not enable Bluetooth transmission making this technology often ineffective for indoor location determination [24].…”

Section: Introductionmentioning

confidence: 99%

Understanding occupancy pattern and improving building energy efficiency through Wi-Fi based indoor positioning

Wang

Shao

2017

Building and Environment

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Detailed visualisation and data analysis of occupancy patterns including spatial distribution and temporal variations are of great importance to delivering energy efficient and productive buildings. An experimental study comprising 24-hour monitoring over 30 full days was conducted in a university library building. Occupancy profiles have been monitored and analysis has been carried out. Central to this monitoring study is the Wi-Fi based indoor positioning system based on the measured Wi-Fi devices' number and locations and data mining methods. Distinct from traditional occupancy and energy studies, more detailed information related to the indoor positions and number of occupants has offered a better understanding of building user behaviour. The implication of the occupancy patterns for energy (e.g. lighting and other building services) efficiency is assessed, assisted with data from lighting sensors where needed. It is found occupancy patterns change dramatically with time. Also, the energy 2 waste patterns have been identified through the method of data association rule mining. If the identified energy waste is removed, the total energy consumption can be reduced by 26.1%. The indoor positioning information also has implications for optimising space use, opening hours as well as staff deployment. The work could be extended to more rooms with diverse functions, other seasons and other types of non-domestic buildings for a more comprehensive understanding of building user behaviour and energy efficiency.

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Research on library lighting intelligent control based on infrared image processing techniques

Cited by 6 publications

References 2 publications

Application of the Machine Vision Technology and Infrared Thermography to the Detection of Hoof Diseases in Dairy Cows: A Review

Application of the Machine Vision Technology and Infrared Thermography to the Detection of Hoof Diseases in Dairy Cows: A Review

Understanding occupancy and user behaviour through Wi-Fi-based indoor positioning

Understanding occupancy pattern and improving building energy efficiency through Wi-Fi based indoor positioning

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