Engineering and Deploying a Cheap Recognition Security System on a Raspberry Pi Platform for a rural Settlement

Abstract

Security is one of the most fundamental challenges of mankind, providing affordable devices for apprehending criminals. Using smart technology is on the rise and the ability to have full surveillance records of both authorised and unauthorized entrance to designated facility or important resource in a timely manner is highly desirable in modern society of today. This paper proposes the use of Histogram of Oriented Gradients (HOG) to train a model capable of recognising authorised personnel on a raspberry pi device for the purpose of security and ease of access to vital infrastructure. HOG was the preferred choice because it is not computationally intensive as compared to Convolutional Neural Networks (CNN) and most other relatively comparable computational algorithms. The HOG network detect faces and sends a report to Firebase Database and an image is also sent to Google Cloud Storage (GCS) a package on the Google Cloud Platform (GCP). Both data from Firebase and GCS are sent to a companion android application where the user can view who entered specific locations, at specific time with accompanying pictorial evidence. The recognition system was deployed on a raspberry pi device that’s feeds in visual data via an inexpensive camera. Collectively, the proposed system is a relatively cheap smart technology security system with inherent ability to accomplish real-time surveillance tasks using widely penetrated android phone technology while maintaining low computational overheads.