Automatic Control of Power Supply in Classroom Using Image Processing

Ganiger, Darshan; Patil, Kishor A.; Patil, Priyanka; Anandhalli, Mallikarjun

doi:10.1109/icraect.2017.16

Cited by 7 publications

(4 citation statements)

References 2 publications

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“…A study [6] presented automatic control of power supply in the classroom, where motion detection was used as the main indication for detecting human presence. Adopting image processing, background subtraction was used to detect the motion created by human presence.…”

Section: A System Automation Based On Person Detectionmentioning

confidence: 99%

Person Detection for Social Distancing and Safety Violation Alert based on Segmented ROI

Ahamad

Zaini

Latip

2020

2020 10th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

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In addressing the worldwide Covid-19 pandemic situation, the process of flattening the curve for coronavirus cases will be difficult if the citizens do not take action to prevent the spread of the virus. One of the most important practices in these outbreaks is to ensure a safe distance between people in public. This paper presents the detection of people with social distance monitoring as a precautionary measure in reducing physical contact between people. This study focuses on detecting people in areas of interest using the MobileNet Single Shot Multibox Detector (SSD) object tracking model and OpenCV library for image processing. The distance will be computed between the persons detected in the captured footage and then compared to a fixed pixels' values. The distance is measured between the central points and the overlapping boundary between persons in the segmented tracking area. With the detection of unsafe distances between people, alerts or warnings can be issued to keep the distance safe. In addition to social distance measure, another key feature of the system is detecting the presence of people in restricted areas, which can also be used to trigger warnings. Some analysis has been performed to test the effectiveness of the program for both purposes. From the results obtained, the distance tracking system achieved between 56.5% to 68% accuracy for testing performed on outdoor and challenging input videos, while 100% accuracy was achieved for the controlled environment on indoor testing. Whereas for the safety violation alert feature based on segmented ROI, it was found to have achieved better accuracy, i.e. between 95.8% to 100% for all tested input videos.

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Section: A System Automation Based On Person Detectionmentioning

confidence: 99%

Person Detection for Social Distancing and Safety Violation Alert based on Segmented ROI

Ahamad

Zaini

Latip

2020

2020 10th IEEE International Conference on Control System, Computing and Engineering (ICCSCE)

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“…An examination [14] introduced programmed control of intensity supply in the study hall, where movement identification was utilized as the principal sign for distinguishing human presence. On receiving picture preparation, foundation deduction was utilized to identify the movement made by the human presence.…”

Section: Automatic System-based Human Detectionmentioning

confidence: 99%

“…The pictures are grouped into two districts, which are the assessment street zone and the zone where the walker should stroll with the proposed strolling human model. The dataset utilized in this task is removed from Caltech and ETH [13,14], web, and creator's own pictures. The area of the picture taken is separated into two districts which are the street zone got from the street path limits.…”

Section: Area-based Human Detectionmentioning

confidence: 99%

Social Distancing and Crowd Density Distribution System for Public Places and Public Transports Using Computer Vision and NLP

Sharma

Modanval

Tayal

et al. 2021

Advanced Computing and Intelligent Technologies

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The world population is growing day by day but the resources are limited. So, on one hand, we have to fulfill our requirements by using resources in an optimized manner, and on the other hand, we are currently in a pandemic for which social distancing is the key to prevent ourselves from the COVID-19 virus. We all travel by bus, train, metros, and other public transports, and we all have faced the problem of overcrowding in public transport once in our life especially during any festive season when there is a lot of rush among people to go from their workplace to their hometown. The problem of overcrowding is very common in developing countries like India where the population is relatively higher than the number of seats in public transports. For instance, if we look at the Delhi metro in which it is almost impossible to travel during the festive season. However, it's a regular problem, but the crowd becomes more when any festive season approaches. And due to such rush, people tend to just get inside the metro without even searching for a relatively less crowded or vacant seat/compartment, resulting in an unusual pattern of density seen across the metro, somewhere empty and somewhere highly crowded that even no place to stand. And now due to the COVID-19 pandemic, the situation worsens, on one hand, we are bound to travel in-crowd for work and on the other hand, we have to maintain social distancing for COVID prevention. So it becomes almost impossible to travel through such public transport. To solve such problems we are proposing a solution, that is, a system that takes care of the public places/transports by scanning and informing passengers where they can move to find a seat or less crowded. It also helps to ensure social distancing in the times of the COVID-19 pandemic.

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“…A study [11] proposed automated power supply monitoring in the classroom, with motion detection as the primary means of detecting human presence. Context subtraction was used to detect the motion caused by human presence using image processing.…”

mentioning

confidence: 99%

Social Distancing Detection in Real Time using Deep Learning and IoT

N¹

2022

IJRASET

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Social Distancing is the practice of maintaining a minimum amount of physical distance from your surrounding people or avoiding direct contact with people or objects in public places so as to minimize the exposure or transmission of diseases like COVID-19. This can also be coupled with wearing of face masks and regular sanitization of hands. The government has made social distancing norms compulsory and to be followed, and penalties would be imposed if violated. But there are a lot of examples of social distancing violations in public places due to negligence of people. There are approaches using bluetooth and mobile phones but it requires an app to be installed. This study provides a method for determining whether the social distancing rule has been broken by combining machine learning models and object detecting techniques. Appropriate actions would be taken in case of any violations detected. In this paper, a comparison is done on all the object detection techniques like Faster R-CNN, YOLO v3, SSD Mobile Net etc. based on accuracy, mean average precision, computational time and ease of integration. Based on this, SSD Mobile Net performed well on accuracy and had a faster computational time. This model can also be deployed on end devices like Raspberry Pi.

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Automatic Control of Power Supply in Classroom Using Image Processing

Cited by 7 publications

References 2 publications

Person Detection for Social Distancing and Safety Violation Alert based on Segmented ROI

Person Detection for Social Distancing and Safety Violation Alert based on Segmented ROI

Social Distancing and Crowd Density Distribution System for Public Places and Public Transports Using Computer Vision and NLP

Social Distancing Detection in Real Time using Deep Learning and IoT

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