Automatic Human Detection Using Reinforced Faster-RCNN for Electricity Conservation System

Ushasukhanya, S.; Karthikeyan, M.

doi:10.32604/iasc.2022.022654

Cited by 6 publications

(1 citation statement)

References 16 publications

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“…To speed up training and for better accuracy in the fast R-CNN, a bounding box regressor is used. In research [58], a real-time human detection system based on the ResNet-50 neural network was described. ResNet was trained using the CHOKEPOINT dataset, which consists of 62,204 images of humans.…”

Section: Human Detection Using Neural Network Systemsmentioning

confidence: 99%

HVAC System Control Solutions Based on Modern IT Technologies: A Review Article

2022

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As energy consumption for building engineering systems is a major part of the total energy spent, it is necessary to reduce it. This leads to the need for the development of new solutions for the control of heating, ventilation, and conditioning (HVAC) systems that are responsive to humans and their demands. In this review article, the existing research and technology advancements of the modern technologies of computer vision and neural networks for application in HVAC control systems are studied. Objectives such as human detection and location, human activity monitoring, skin temperature detection, and clothing level detection systems are important for the operation of precise, high-tech HVAC systems. This article tries to compile the latest achievements and principal solutions on how this information is acquired. Moreover, it how parameters such as indoor air quality (IAQ), variable air volume ventilation, computer vision, metabolic rate, and human clothing isolation can affect final energy consumption is studied. The research studies discussed in this review article have been tested in real application scenarios and prove the benefits of using a particular technology in ventilation systems. As a result, the modernized control systems have shown advantages over the currently applied typical non-automated systems by providing higher IAQ and reducing unnecessary energy consumption.

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Section: Human Detection Using Neural Network Systemsmentioning

confidence: 99%