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Improving video surveillance systems in banks using deep learning techniques
| dc.contributor.author | Zahrawi, Mohammad | |
| dc.contributor.author | Shaalan, Khaled | |
| dc.date.accessioned | 2025-02-10T05:26:32Z | |
| dc.date.available | 2025-02-10T05:26:32Z | |
| dc.date.issued | 2023 | |
| dc.description.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. | |
| dc.identifier.uri | https://bspace.buid.ac.ae/handle/1234/2783 | |
| dc.language.iso | en | |
| dc.title | Improving video surveillance systems in banks using deep learning techniques | |
| dc.type | Article |
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