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Improving video surveillance systems in banks using deep learning techniques
Abstract
In the contemporary world, security and safety are signifcant concerns for any country that wants
to succeed in tourism, attracting investors, and economics. Manually, guards monitoring 24/7 for
robberies or crimes becomes an exhaustive task, and real-time response is essential and helpful for
preventing armed robberies at banks, casinos, houses, and ATMs. This paper presents a study based
on real-time object detection systems for weapons auto-detection in video surveillance systems.
We propose an early weapon detection framework using state-of-the-art, real-time object detection
systems such as YOLO and SSD (Single Shot Multi-Box Detector). In addition, we considered closely
reducing the number of false alarms in order to employ the model in real-life applications. The model
is suitable for indoor surveillance cameras in banks, supermarkets, malls, gas stations, and so forth.
The model can be employed as a precautionary system to prevent robberies by implying the model in
outdoor surveillance cameras.