Monitoring Systems for Counting People using Raspberry Pi 3

Rantelobo, Kalvein; Indraswara, M. A.; Sastra, Nyoman Putra; Wiharta, Dewa Made; Lami, Hendro Fj; Kotta, Herry Z.

doi:10.1109/icsgteis.2018.8709141

Cited by 10 publications

(6 citation statements)

References 5 publications

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“…The scheme proposed in this paper is as shown in Figure 6. The project we have tested in various previous activities [36]. As for the scheme we developed in this paper using the H.265 / HEVC video coding standard.…”

Section: Overview Of the Scheme Of Proposed And Primarily Resultsmentioning

confidence: 99%

“…The main results of this research scheme only arrived at monitoring moving objects that had a simple movement, namely the flow in and out of a room. We have published the results [36], and the results we will release will be in the form of journals based on the scheme of this paper as shown in Figure 8. The test results from the development scheme are expected to apply in various fields for monitoring objects and even surveillance activities.…”

Section: Proceedingmentioning

confidence: 99%

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Proceeding ICEST Labuan Bajo 2018

SIANTURI¹

2023

Preprint

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Section: Overview Of the Scheme Of Proposed And Primarily Resultsmentioning

confidence: 99%

Section: Proceedingmentioning

confidence: 99%

Proceeding ICEST Labuan Bajo 2018

SIANTURI¹

2023

Preprint

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“…The objective of this study is to develop a monitoring system for population counting in a room using a visual sensor in a Wireless Sensor Network (WSN) environment. The system can determine the number ofpeople in the room from the image processing results and then send that information to the web [9].…”

Section: A Object Detection Modelsmentioning

confidence: 99%

Enhancing Commuting Experience for Students and Faculty: A Transportation Kit

Poornimathi,

Dhayalan,

2023

Recent Trends in Data Science and Its Applications

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Students and faculty members of any college on a day-to-day basis use the buses provided by the college to commute.There is a lot of meticulous planning that goes into this. The data required for planning will surmount to decisions made such as how many buses should run today, the routes they need to take and have the buses reached the college premises. A kit has been developed in order to ease the work of the transportation department. Using this data collection will help in enhancing the experience of the passengers and also make the work of the transportation department easier. The two features that come in the transportation kit are: a license plate detection model to detect the number plate of buses and cars entering the college, which is developed using OCR and YOLOV7 model in machine learning and a bus tracking mobile application to track buses for students and faculty members, which is developed using Flutter and Firebase.

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“…In this way, the characteristics of interest are extracted and the decision process is improved. Also, Rantelobo et al [16], developed a monitoring and people-counting system using an embedded Raspberry Pi 3 board, through image processing with web server communication. The processing consists of stages of conversion to grey, segmentation and subtraction of the image background, morphological operations and approximation of the image contour area.…”

Section: Literature Reviewmentioning

confidence: 99%

Background subtraction and yolo algorithm

Rondón¹,

Casadiego²,

Delgado³

2021

ing. Solidar

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Introduction: This article is the result of research entitled “Signal processing system for the detection of people in agglomerations in areas of public space in the city of Cúcuta”, developed at the Universidad Francisco de Paula Santander in 2020.Problem: The high percentage of false positives and false negatives in people detection processes makes decision making in video surveillance, tracking and tracing applications complex. Objective: To determine which technique for the detection of people presents better results in terms of respon-se time and detection hits.Methodology: Two techniques for the detection of people in uncontrolled environments are validated in Python with videos taken inside the Universidad Francisco de Paula Santander: Background subtraction and the YOLO algorithm.Results: With the background subtraction technique, we obtained a hit rate of 84.07 % and an average response time of 0.815 seconds. Likewise, with the YOLO algorithm the hit rate and average response time are 90% and 4.59 seconds respectively.Conclusion: It is possible to infer the use of the background subtraction technique in hardware tools such as the Pi 3B+ Raspberry board for processes in which the analysis of information in real time is prioritized, while the YOLO algorithm presents the characteristics required in the processes in which the information is analyzed after the acquisition of the image.Originality: Through this research, aspects required for the real-time analysis of information obtained in pro-cesses of people detection in uncontrolled environments were analyzed. Limitations: The analyzed videos were taken only at the Universidad Francisco de Paula Santander. Also, the Raspberry Pi 3B+ board overheats when processing the video images, due to the full resource requirement of the device.

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Monitoring Systems for Counting People using Raspberry Pi 3

Cited by 10 publications

References 5 publications

Proceeding ICEST Labuan Bajo 2018

Proceeding ICEST Labuan Bajo 2018

Enhancing Commuting Experience for Students and Faculty: A Transportation Kit

Background subtraction and yolo algorithm

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