Computer vision to advance the sensing and control of built environment towards occupant-centric sustainable development: A critical review
暂无分享,去创建一个
[1] M. Al-Hussein,et al. Computer vision applications in offsite construction , 2023, Automation in Construction.
[2] Junqi Wang,et al. Intelligent ventilation control in enclosed environment towards health and energy efficiency: a study of elevator cabins , 2023, Energy and Buildings.
[3] Faming Wang,et al. A Novel Occupant-Centric Stratum Ventilation System Using Computer Vision: Occupant Detection, Thermal Comfort, Air Quality, and Energy Savings , 2023, SSRN Electronic Journal.
[4] Nianyin Zeng,et al. A novel attention-based enhancement framework for face mask detection in complicated scenarios , 2023, Signal Processing: Image Communication.
[5] J. Moon,et al. Seasonal effects of thermal comfort control considering real-time clothing insulation with vision-based model , 2023, Building and Environment.
[6] F. Haghighat,et al. Intelligent operation, maintenance, and control system for public building: Towards infection risk mitigation and energy efficiency , 2023, Sustainable Cities and Society.
[7] M. Nappi,et al. Face mask detection using deep convolutional neural network and multi-stage image processing , 2023, Image and Vision Computing.
[8] B. Selvakumar,et al. A Unified Framework for Monitoring Social Distancing and Face Mask Wearing Using Deep Learning: An Approach to Reduce COVID-19 Risk , 2023, Procedia Computer Science.
[9] F. Haghighat,et al. Towards self-learning control of HVAC systems with the consideration of dynamic occupancy patterns: Application of model-free deep reinforcement learning , 2022, Building and Environment.
[10] J. Poutanen,et al. Resilience in the built environment: key characteristics for solutions to multiple crises , 2022, Sustainable Cities and Society.
[11] Peiyong Duan,et al. Smart lighting control system based on fusion of monocular depth estimation and multi-object detection , 2022, Energy and Buildings.
[12] Yingdong He,et al. Infrared-Fused Vision-Based Thermoregulation Performance Estimation for Personal Thermal Comfort-Driven HVAC System Controls , 2022, Buildings.
[13] T. Khattab,et al. Deep visual social distancing monitoring to combat COVID-19: A comprehensive survey , 2022, Sustainable Cities and Society.
[14] Gregory Dudek,et al. Fidora: Robust WiFi-Based Indoor Localization via Unsupervised Domain Adaptation , 2022, IEEE Internet of Things Journal.
[15] Deepankar Kumar Ashish,et al. A review on sustainable use of agricultural straw and husk biomass ashes: Transitioning towards low carbon economy. , 2022, The Science of the total environment.
[16] L. Bellia,et al. Virtual reality for assessing visual quality and lighting perception: A systematic review , 2022, Building and Environment.
[17] Enting Gao,et al. Metabolism-based ventilation monitoring and control method for COVID-19 risk mitigation in gymnasiums and alike places , 2022, Sustainable Cities and Society.
[18] M. Chew,et al. A Review of Infrared Thermography for Delamination Detection on Infrastructures and Buildings , 2022, Sensors.
[19] B. Dong,et al. From occupants to occupants: A review of the occupant information understanding for building HVAC occupant-centric control , 2021, Building Simulation.
[20] Taeyeon Kim,et al. Review of vision-based occupant information sensing systems for occupant-centric control , 2021 .
[21] Taeyeon Kim,et al. Application of vision-based occupancy counting method using deep learning and performance analysis , 2021 .
[22] Sandro Nižetić,et al. Smart monitoring technologies for personal thermal comfort: A review , 2021 .
[23] Shailesh D. Kamble,et al. A Review on COVID-19 Face Mask Detection using CNN , 2021, Journal of Pharmaceutical Research International.
[24] Guijin Wang,et al. Image-based occupancy positioning system using pose-estimation model for demand-oriented ventilation , 2021, Journal of Building Engineering.
[25] Alireza Ahmadian Fard Fini,et al. Computer vision-based interior construction progress monitoring: A literature review and future research directions , 2021, Automation in Construction.
[26] Yi Wang,et al. Smart air supply terminal for floor-standing room air conditioners based on the identification of human positions , 2021 .
[27] John Kaiser Calautit,et al. A Deep Learning Approach Towards the Detection and Recognition of Opening of Windows for Effective Management of Building Ventilation Heat Losses and Reducing Space Heating Demand , 2021 .
[28] David Ndzi,et al. Vision Based Dynamic Thermal Comfort Control Using Fuzzy Logic and Deep Learning , 2021, Applied Sciences.
[29] Shelly L. Miller,et al. A paradigm shift to combat indoor respiratory infection , 2021, Science.
[30] Deepankar Kumar Ashish,et al. Aerogel based thermal insulating cementitious composites: A review , 2021 .
[31] Fariborz Haghighat,et al. Occupancy-based HVAC control systems in buildings: A state-of-the-art review , 2021, Building and Environment.
[32] Hong Yang,et al. Reference-free video-to-real distance approximation-based urban social distancing analytics amid COVID-19 pandemic , 2021, Journal of Transport & Health.
[33] Lyudmila Mihaylova,et al. A deep learning framework for autonomous flame detection , 2021, Neurocomputing.
[34] Fakhrul Alam,et al. Device-Free Localization: A Review of Non-RF Techniques for Unobtrusive Indoor Positioning , 2021, IEEE Internet of Things Journal.
[35] F. Haghighat,et al. Occupant-density-detection based energy efficient ventilation system: Prevention of infection transmission , 2021, Energy and Buildings.
[36] Xuan Li,et al. Development of a novel method to detect clothing level and facial skin temperature for controlling HVAC systems , 2021 .
[37] F. Haghighat,et al. Extracting energy-related knowledge from mining occupants’ behavioral data in residential buildings , 2021, Journal of Building Engineering.
[38] Gwanggil Jeon,et al. Social distance monitoring framework using deep learning architecture to control infection transmission of COVID-19 pandemic , 2021, Sustainable Cities and Society.
[39] Gongsheng Huang,et al. Data mining approach for improving the optimal control of HVAC systems: An event-driven strategy , 2021, Journal of Building Engineering.
[40] Jin Woo Moon,et al. Development of a Deep Neural Network Model for Estimating Joint Location of Occupant Indoor Activities for Providing Thermal Comfort , 2021, Energies.
[41] R. Socher,et al. Deep learning-enabled medical computer vision , 2021, npj Digital Medicine.
[42] W. Nazaroff. Residential air-change rates: A critical review. , 2021, Indoor air.
[43] Zheng O'Neill,et al. Nationwide HVAC energy-saving potential quantification for office buildings with occupant-centric controls in various climates , 2020 .
[44] Jinwoo Kim,et al. Visual Analytics for Operation-Level Construction Monitoring and Documentation: State-of-the-Art Technologies, Research Challenges, and Future Directions , 2020, Frontiers in Built Environment.
[45] Naglaa A. Megahed,et al. Indoor Air Quality: Rethinking rules of building design strategies in post-pandemic architecture , 2020, Environmental Research.
[46] Nour Eldeen M. Khalifa,et al. Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection , 2020, Sustainable Cities and Society.
[47] M. Shamim Hossain,et al. Towards the sustainable development of smart cities through mass video surveillance: A response to the COVID-19 pandemic , 2020, Sustainable Cities and Society.
[48] John Kaiser Calautit,et al. Vision-based detection and prediction of equipment heat gains in commercial office buildings using a deep learning method , 2020 .
[49] John Kaiser Calautit,et al. A vision-based deep learning approach for the detection and prediction of occupancy heat emissions for demand-driven control solutions , 2020, Energy and Buildings.
[50] H. Jouhara,et al. Review of ventilation strategies to reduce the risk of disease transmission in high occupancy buildings , 2020, International Journal of Thermofluids.
[51] Abbas Javed,et al. Occupancy detection in non-residential buildings – A survey and novel privacy preserved occupancy monitoring solution , 2020, Applied Computing and Informatics.
[52] Mohammad Yusri Hassan,et al. Lighting system control techniques in commercial buildings: Current trends and future directions , 2020 .
[53] Pedro Arias,et al. Thermographic methodologies used in infrastructure inspection: A review—data acquisition procedures , 2020 .
[54] S. K. Adhikary,et al. Development of flowable ultra-lightweight concrete using expanded glass aggregate, silica aerogel, and prefabricated plastic bubbles , 2020 .
[55] B. Hughes,et al. Revolutionising building inspection techniques to meet large-scale energy demands: A review of the state-of-the-art , 2020 .
[56] Jingsi Zhang,et al. Review on occupant-centric thermal comfort sensing, predicting, and controlling , 2020 .
[57] Sinan Kalkan,et al. Image-based construction of building energy models using computer vision , 2020, Automation in Construction.
[58] Ivan Mutis,et al. Real-time space occupancy sensing and human motion analysis using deep learning for indoor air quality control , 2020 .
[59] Sousso Kelouwani,et al. A comprehensive review of approaches to building occupancy detection , 2020 .
[60] Sharon J Peacock,et al. Pathophysiology, Transmission, Diagnosis, and Treatment of Coronavirus Disease 2019 (COVID-19): A Review. , 2020, JAMA.
[61] Shen Wei,et al. A prediction model coupling occupant lighting and shading behaviors in private offices , 2020, Energy and Buildings.
[62] Jianhong Zou,et al. A review of building occupancy measurement systems , 2020 .
[63] Pedro Arias,et al. Thermographic methodologies used in infrastructure inspection: A review—Post-processing procedures , 2020 .
[64] Tuan Ngo,et al. Sensor-based safety management , 2020 .
[65] Huan Wang,et al. Non-invasive (non-contact) measurements of human thermal physiology signals and thermal comfort/discomfort poses -A review , 2020, Energy and Buildings.
[66] Abhishek Kumar Singh,et al. Video Flame and Smoke Based Fire Detection Algorithms: A Literature Review , 2020, Fire Technology.
[67] Liu Guanghui,et al. Real-time dynamic estimation of occupancy load and an air-conditioning predictive control method based on image information fusion , 2020 .
[68] B. Cowling,et al. Respiratory virus shedding in exhaled breath and efficacy of face masks , 2020, Nature Medicine.
[69] Carol C. Menassa,et al. Robust non-intrusive interpretation of occupant thermal comfort in built environments with low-cost networked thermal cameras , 2019, Applied Energy.
[70] Dengxin Dai,et al. Real-time and contactless measurements of thermal discomfort based on human poses for energy efficient control of buildings , 2019, Building and Environment.
[71] B. Becerik-Gerber,et al. A comparative study of predicting individual thermal sensation and satisfaction using wrist-worn temperature sensor, thermal camera and ambient temperature sensor , 2019, Building and Environment.
[72] Taeyeon Kim,et al. Development of a human metabolic rate prediction model based on the use of Kinect-camera generated visual data-driven approaches , 2019, Building and Environment.
[73] Subhas Chandra Mukhopadhyay,et al. Fire Sensing Technologies: A Review , 2019, IEEE Sensors Journal.
[74] Gail Brager,et al. Analysis of the accuracy on PMV – PPD model using the ASHRAE Global Thermal Comfort Database II , 2019, Building and Environment.
[75] Stéphane Ploix,et al. Unmasking the causal relationships latent in the interplay between occupant’s actions and indoor ambience: A building energy management outlook , 2019, Applied Energy.
[76] Junqi Wang,et al. Wi-Fi based occupancy detection in a complex indoor space under discontinuous wireless communication: A robust filtering based on event-triggered updating , 2019, Building and Environment.
[77] Zoltán Nagy,et al. LightLearn: An adaptive and occupant centered controller for lighting based on reinforcement learning , 2019, Building and Environment.
[78] Yaser Sheikh,et al. OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields , 2018, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[79] Luc Van Gool,et al. Non-invasive thermal comfort perception based on subtleness magnification and deep learning for energy efficiency , 2018, ArXiv.
[80] Heangwoo Lee,et al. Development of a Dimming Lighting Control System Using General Illumination and Location-Awareness Technology , 2018, Energies.
[81] Rahul Simha,et al. Thermal comfort modeling in transient conditions using real-time local body temperature extraction with a thermographic camera , 2018, Building and Environment.
[82] Carol C. Menassa,et al. Non-intrusive interpretation of human thermal comfort through analysis of facial infrared thermography , 2018, Energy and Buildings.
[83] Farrokh Jazizadeh,et al. Vision-based thermal comfort quantification for HVAC control , 2018, Building and Environment.
[84] Anita M. M. Liu,et al. Effects of neighborhood building density, height, greenspace, and cleanliness on indoor environment and health of building occupants , 2018, Building and Environment.
[85] C. Lerma,et al. A discussion concerning active infrared thermography in the evaluation of buildings air infiltration , 2018, Energy and Buildings.
[86] Farrokh Jazizadeh,et al. Personalized thermal comfort inference using RGB video images for distributed HVAC control , 2018, Applied Energy.
[87] Nikolaos Doulamis,et al. Deep Learning for Computer Vision: A Brief Review , 2018, Comput. Intell. Neurosci..
[88] Burcin Becerik-Gerber,et al. Towards unsupervised learning of thermal comfort using infrared thermography , 2018 .
[89] Jerzy Robert Ładny,et al. The use of drones during mass events , 2017 .
[90] Guoqing Liu,et al. A pilot study of online non-invasive measuring technology based on video magnification to determine skin temperature , 2017 .
[91] Shiming Ge,et al. Detecting Masked Faces in the Wild with LLE-CNNs , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[92] Qingqing Feng,et al. Predictive control of indoor environment using occupant number detected by video data and CO2 concentration , 2017 .
[93] Xinbo Ruan,et al. A Review of LED Drivers and Related Technologies , 2017, IEEE Transactions on Industrial Electronics.
[94] K. Ling,et al. A tracking cooling fan using geofence and camera-based indoor localization , 2017 .
[95] Steve Goodhew,et al. Building defect detection: External versus internal thermography , 2016 .
[96] Hua Li,et al. Indoor occupancy estimation from carbon dioxide concentration , 2016, ArXiv.
[97] Burcin Becerik-Gerber,et al. Energy savings from temperature setpoints and deadband: Quantifying the influence of building and system properties on savings , 2016 .
[98] W Wim Zeiler,et al. Occupancy measurement in commercial office buildings for demand-driven control applications : a survey and detection system evaluation , 2015 .
[99] Jan-Olof Dalenbäck,et al. CO2 sensors for occupancy estimations: Potential in building automation applications , 2014 .
[100] Girish Ghatikar,et al. Miscellaneous and Electronic Loads Energy Efficiency Opportunities for Commercial Buildings: A Collaborative Study by the United States and India , 2014 .
[101] Burcin Becerik-Gerber,et al. User-led decentralized thermal comfort driven HVAC operations for improved efficiency in office buildings , 2014 .
[102] Frédo Durand,et al. Eulerian video magnification for revealing subtle changes in the world , 2012, ACM Trans. Graph..
[103] Michael Sheppy,et al. Extracting Operating Modes from Building Electrical Load Data , 2011, 2011 IEEE Green Technologies Conference (IEEE-Green).
[104] Hélène Laurent,et al. Towards a sensor for detecting human presence and characterizing activity , 2011 .
[105] Z. Lian,et al. Evaluation of calculation methods of mean skin temperature for use in thermal comfort study , 2011 .
[106] O. T. Masoso,et al. The dark side of occupants’ behaviour on building energy use , 2010 .
[107] Alberto E. Cerpa,et al. Energy efficient building environment control strategies using real-time occupancy measurements , 2009, BuildSys '09.
[108] Takuji Suzuki,et al. Estimation of thermal sensation using human peripheral skin temperature , 2009, 2009 IEEE International Conference on Systems, Man and Cybernetics.
[109] R. Becker,et al. Thermal comfort in residential buildings – Failure to predict by Standard model , 2009 .
[110] Hui Zhang,et al. Observations of upper-extremity skin temperature and corresponding overall-body thermal sensations and comfort , 2007 .
[111] E. de Oliveira Fernandes,et al. Perceived health and comfort in relation to energy use and building characteristics , 2006 .
[112] Thananchai Leephakpreeda,et al. Adaptive Occupancy-based Lighting Control via Grey Prediction , 2005 .
[113] Mads Mysen,et al. Demand controlled ventilation for office cubicles—can it be profitable? , 2003 .
[114] Anibal T. de Almeida,et al. Sensor-based demand-controlled ventilation: a review , 1998 .
[115] S. Tučkutė,et al. Characterization of novel lightweight self-compacting cement composites with incorporated expanded glass, aerogel, zeolite and fly ash , 2022, Case Studies in Construction Materials.
[116] K. Kotecha,et al. A Review on Machine Learning Styles in Computer Vision—Techniques and Future Directions , 2022, IEEE Access.
[117] Maciej Nikodem,et al. Channel Diversity for Indoor Localization using Bluetooth Low Energy and Extended Advertisements , 2021, IEEE Access.
[118] W Wim Zeiler,et al. Experimental evaluation of the performance of chair sensors in an office space for occupancy detection and occupancy-driven control , 2016 .
[119] Burcin Becerik-Gerber,et al. Human-Building Interaction Framework for Personalized Thermal Comfort-Driven Systems in Office Buildings , 2014, J. Comput. Civ. Eng..
[120] Walter G. Sannita,et al. Heart rate variability, homeostasis, and brain function: A tutorial and review of application. , 2012 .
[121] Shengwei Wang,et al. In-situ implementation and validation of a CO2-based adaptive demand-controlled ventilation strategy in a multi-zone office building , 2011 .
[122] Christopher K. I. Williams,et al. International Journal of Computer Vision manuscript No. (will be inserted by the editor) The PASCAL Visual Object Classes (VOC) Challenge , 2022 .