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.
