HUMAN BODY POSE DETECTION USING DEEP LEARNING FOR SMART HOME SURVEILLANCE SYSTEM - Miqdad Nadra, Muhammad
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.