ATM Cash Trapping Detection via Computer Vision
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Solution Overview
Problem
Current methods for preventing and detecting cash trapping at ATMs are costly and require hardware modifications, lacking viable non-hardware-based solutions, which complicates fraud detection and customer access to their funds.
Innovation Solution
A system utilizing existing cameras to capture and analyze images of the cash dispense module and surrounding area, employing machine-learning algorithms and computer vision to detect cash trapping devices and activities, sending alerts and remotely deactivating the ATM if necessary, without requiring additional hardware changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hardware changes and additional sensors are added to the ATM to detect cash trapping, then detection capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces physical hardware sensors with computer vision technology using existing cameras. The system captures images of the cash dispense module and uses image processing algorithms to detect cash trapping devices, eliminating the need for additional mechanical sensors while maintaining detection capability.
Solution Approach 2:
The system creates a visual copy or representation of the cash dispense module through camera imaging. By analyzing the image data rather than physical sensor data, the system can detect anomalies indicating cash trapping without adding physical detection hardware to the ATM.
2Ease of manufacture
If existing cameras are used for cash trapping detection, then cost is reduced and hardware modifications are avoided, but detection precision may be insufficient
Solution Approach 1:
The patent transforms the data parameters from simple image capture to processed image features. By extracting specific visual features from camera images and analyzing patterns in the image data, the system achieves high detection precision using standard cameras without requiring specialized high-precision sensors.
Solution Approach 2:
The system introduces image processing algorithms as an intermediary between the camera and detection decision. These algorithms enhance the raw image data, extracting meaningful features that indicate cash trapping, thereby bridging the gap between standard camera capability and high-precision detection requirements.
Data Source
AI summary
Cash trapping at an Automated Teller Machine (ATM) is detected in real time. One or more images from one or more cameras are analyzed. The camera(s) is/are focused overhead of the ATM or on a cash slot of a dispense module for the ATM. The images are analyzed for determining one or more of whether the dispenser module is authentic, whether the cash slot opened or did not open when it should have opened for a cash withdraw, whether visual features of the dispense module have changed over a configurable period of time, and whether hands, gestures, and actions of a person present at the ATM indicate that cash trapping is taking place at the ATM. When cash trapping is detected at the ATM a variety of automated actions are processed, such as shutting down the dispense module, shutting down the ATM, notifying a financial institution, and/or notifying legal authorities.