HUMAN BODY POSE DETECTION USING DEEP LEARNING FOR SMART HOME SURVEILLANCE SYSTEM

Developing countries cannot be separated from high crime rates, data from the Central Statistics Agency (BPS) in 2018 the percentage of the population of victims of theft crimes by the province in Indonesia is at 84.48%, this figure continues to increase every year based on BPS data in 2020 the perc...

全面介紹

Saved in:
書目詳細資料
主要作者: Miqdad Nadra, Muhammad
格式: Final Project
語言:Indonesia
在線閱讀:https://digilib.itb.ac.id/gdl/view/64072
標簽: 添加標簽
沒有標簽, 成為第一個標記此記錄!
機構: Institut Teknologi Bandung
語言: Indonesia
id id-itb.:64072
spelling id-itb.:640722022-03-28T12:28:13ZHUMAN BODY POSE DETECTION USING DEEP LEARNING FOR SMART HOME SURVEILLANCE SYSTEM Miqdad Nadra, Muhammad Indonesia Final Project Smart Home Surveillance System, Deep learning, Human Body Pose Detection, Data Communication, Mobile Applications, Convolutional Neural Network. INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/64072 Developing countries cannot be separated from high crime rates, data from the Central Statistics Agency (BPS) in 2018 the percentage of the population of victims of theft crimes by the province in Indonesia is at 84.48%, this figure continues to increase every year based on BPS data in 2020 the percentage the population of victims of theft by the province in Indonesia rose to 86.51% which makes this even more worrying. The increase in crime is not followed by the readiness of the community to improve the security system to anticipate these crimes. In this final project, a residential surveillance system is created as part of a suspicious action detection system that is integrated with a cellular application using an external camera based on human body pose detection with the deep learning method of the Convolutional Neural Network (CNN). The system works by detecting the image captured from the camera, classifying the type of movement, determining the status of the detected motion, and providing notifications that are forwarded using the concept of data communication to the user when a dangerous situation is detected by the system through a mobile application. Based on the experiments and tests carried out, the dataset was created with a total data of 6000 samples which were divided into 6 movements with the division into 2 statuses, namely safe and unsafe. The model's performance after being tested resulted in an accuracy of 96.8%, error 3.2%, precision 90.4%, recall 90.4%, and f1-score 90.4% which indicated that the model was good and there was no tendency for the model to classify to a certain class. In the movement status, a simple system has also been successfully built with the addition of if and else thresholds with the status output for each movement by their respective classes. Overall the dataset generation system, movement type classification system, and movement status classification system can meet the expected specifications. text
institution Institut Teknologi Bandung
building Institut Teknologi Bandung Library
continent Asia
country Indonesia
Indonesia
content_provider Institut Teknologi Bandung
collection Digital ITB
language Indonesia
description Developing countries cannot be separated from high crime rates, data from the Central Statistics Agency (BPS) in 2018 the percentage of the population of victims of theft crimes by the province in Indonesia is at 84.48%, this figure continues to increase every year based on BPS data in 2020 the percentage the population of victims of theft by the province in Indonesia rose to 86.51% which makes this even more worrying. The increase in crime is not followed by the readiness of the community to improve the security system to anticipate these crimes. In this final project, a residential surveillance system is created as part of a suspicious action detection system that is integrated with a cellular application using an external camera based on human body pose detection with the deep learning method of the Convolutional Neural Network (CNN). The system works by detecting the image captured from the camera, classifying the type of movement, determining the status of the detected motion, and providing notifications that are forwarded using the concept of data communication to the user when a dangerous situation is detected by the system through a mobile application. Based on the experiments and tests carried out, the dataset was created with a total data of 6000 samples which were divided into 6 movements with the division into 2 statuses, namely safe and unsafe. The model's performance after being tested resulted in an accuracy of 96.8%, error 3.2%, precision 90.4%, recall 90.4%, and f1-score 90.4% which indicated that the model was good and there was no tendency for the model to classify to a certain class. In the movement status, a simple system has also been successfully built with the addition of if and else thresholds with the status output for each movement by their respective classes. Overall the dataset generation system, movement type classification system, and movement status classification system can meet the expected specifications.
format Final Project
author Miqdad Nadra, Muhammad
spellingShingle Miqdad Nadra, Muhammad
HUMAN BODY POSE DETECTION USING DEEP LEARNING FOR SMART HOME SURVEILLANCE SYSTEM
author_facet Miqdad Nadra, Muhammad
author_sort Miqdad Nadra, Muhammad
title HUMAN BODY POSE DETECTION USING DEEP LEARNING FOR SMART HOME SURVEILLANCE SYSTEM
title_short HUMAN BODY POSE DETECTION USING DEEP LEARNING FOR SMART HOME SURVEILLANCE SYSTEM
title_full HUMAN BODY POSE DETECTION USING DEEP LEARNING FOR SMART HOME SURVEILLANCE SYSTEM
title_fullStr HUMAN BODY POSE DETECTION USING DEEP LEARNING FOR SMART HOME SURVEILLANCE SYSTEM
title_full_unstemmed HUMAN BODY POSE DETECTION USING DEEP LEARNING FOR SMART HOME SURVEILLANCE SYSTEM
title_sort human body pose detection using deep learning for smart home surveillance system
url https://digilib.itb.ac.id/gdl/view/64072
_version_ 1823647377772249088