ATM Video Based ATM Detection CNN Video Based Prediction in Python Projects

ATM Video Based ATM Detection CNN Video Based Prediction in Python Projects
Abstract
ATMs (Automated Teller Machines) are vulnerable to fraud, unauthorized access, and suspicious activities, making real-time monitoring and detection an essential requirement for banking security. Manual monitoring of CCTV footage is often ineffective due to human error, fatigue, and delayed responses. This project, ATM Video-Based Detection using CNN Video Prediction in Python, proposes a deep learning–based surveillance system that uses Convolutional Neural Networks (CNNs) to automatically analyze ATM video feeds. The system extracts frames from live or recorded ATM CCTV footage, processes them, and classifies activities into normal or abnormal events. By leveraging OpenCV, TensorFlow/Keras, NumPy, and Matplotlib, the project builds a scalable and automated detection system that can significantly improve ATM security through real-time anomaly detection and alert generation.

Existing System
Current ATM monitoring systems rely heavily on manual surveillance by security personnel or use basic motion detection algorithms. While motion detection can identify unusual activity, it is prone to false positives (such as when a customer lingers longer) and false negatives (when malicious activity resembles normal ATM use). Manual monitoring also introduces challenges like slow response times, fatigue, and human error, especially when monitoring multiple ATMs simultaneously. Furthermore, existing systems lack intelligent classification to differentiate between legitimate transactions and fraudulent behavior.

Proposed System

The proposed system introduces an automated ATM detection model that uses CNN-based video classification to improve accuracy and reliability. The system preprocesses video streams using OpenCV, extracts frames, and applies the CNN model to classify user behavior as normal (e.g., standard withdrawals) or abnormal (e.g., tampering, multiple unauthorized persons, covering cameras). The model is trained on labeled datasets of ATM surveillance videos and evaluated using accuracy, precision, recall, and F1-score. When suspicious activity is detected, the system can trigger real-time alerts to notify authorities. Compared to existing systems, this approach provides automation, scalability, real-time performance, and reduced false alarms, making it highly effective for large-scale ATM networks.

Customer Reviews (0)

Share Your Experience

Rating

Your Comment

Submit Review Cancel

No reviews yet. Be the first!