ARTIFICIAL INTELLIGENCE-BASED DETECTION OF SUSPICIOUS ACTIVITY IN ATM USING YOLO ALGORITHM

ARTIFICIAL INTELLIGENCE-BASED DETECTION OF SUSPICIOUS ACTIVITY IN ATM USING YOLO ALGORITHM

Mrs.Keerthana A. V
Assistant Professor, Dept of CSE
Adhiyamaan College of Engineering(Autonomous)
Hosur, India

Sivanandhan S, Thennarasu C, Vishwa A
4th Year, Dept of CSE
Adhiyamaan College of Engineering(Autonomous)
Hosur, India

Abstract— In our daily life, we see a lot of crimes and theft taking place which is not known properly. The major crimes are happening at the ATM. Developing several security and cops isn’t an effective answer to conquer the existing issue on the ATMs that the general population who are not engaged in criminal exercises will not feel free to use ATMs and social services. In this project, we use artificial intelligence techniques to detect abnormal events that occur in the ATM. In this project weapon detection and monitoring have major applications of computer vision to tackle various problems. Due to the growing demand for the protection of safety, security, and personal properties, the need and deployment of video surveillance systems that can recognize and interpret the scene and anomaly events play a vital role in intelligence monitoring. Knife or certain pattern detection is the identification of irregular, unexpected, unpredictable, unusual events or items, which is not considered as a normally occurring event or a regular item in a pattern or items present in a dataset and thus different from existing patterns. This project implements automatic knife, and gun detection in ATMs using a yolo algorithm. Our Proposed implementation uses a dataset of pre-labeled images with objects of interest. The algorithms achieve good accuracy, but their application in real situations can be based on the trade-off between speed and accuracy.

Keywords— ATM, R-CNN, Retina-Net, and Single-Shot MultiBox Detector (SSD)