Invalid Camera Detection via Background Feature Correlation
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Solution Overview
Problem
Video surveillance systems rely on manual detection by security personnel to identify when a camera has been moved or covered, leading to delays and potential security risks due to the lack of real-time invalid camera detection.
Innovation Solution
A system that automatically analyzes video images by learning the background scene, extracting feature points, and correlating them to detect significant changes, determining a camera as invalid based on thresholds for non-correlated features, thereby identifying invalid cameras quickly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If manual detection by security personnel is used to identify invalid cameras, then the system is simple to operate, but the detection speed is slow and security risks increase
Solution Approach 1:
The system performs self-diagnosis by automatically detecting its own operational status. The camera system compares captured images against stored reference images and autonomously identifies when it has become invalid (moved or covered), eliminating the need for continuous manual monitoring while maintaining fast detection speed.
Solution Approach 2:
The manual mechanical inspection process is replaced with an automated image processing system. The computer compares digital image data using correlation algorithms to detect camera status changes, substituting human visual inspection with automated optical and computational methods that provide faster, more consistent detection.
2Reliability
If security personnel actively monitor all video displays simultaneously, then invalid cameras can be detected quickly, but the workload and complexity of operation increase significantly
Solution Approach 1:
The camera system autonomously monitors its own validity status by continuously comparing current images with reference images. This self-diagnosis capability ensures high security reliability without requiring security personnel to actively watch all camera feeds, making the system easier to operate while maintaining detection effectiveness.
Solution Approach 2:
The system implements automatic feedback by comparing real-time image capture with stored reference images and generating alerts when discrepancies indicate camera movement or obstruction. This closed-loop feedback mechanism maintains high security reliability by immediately detecting invalid cameras while reducing operational complexity through automated notification rather than continuous human monitoring.
3Reliability
If there is a delay in detecting invalid cameras, then the system requires less complex detection mechanisms, but security risks are compromised
Solution Approach 1:
Manual security monitoring is replaced with automated image correlation technology. The computer system uses digital image processing and correlation algorithms to detect camera status changes, providing immediate detection that mitigates security risks while maintaining reasonable system complexity through software-based rather than human-based detection.
Solution Approach 2:
The system autonomously detects its own invalidation by comparing current images with reference images and automatically generating alerts. This self-monitoring capability ensures continuous security reliability without requiring complex external detection mechanisms or human intervention, achieving fast detection with moderate system complexity.
Data Source
AI summary
A system having a camera (18) for capturing video images of a scene in successive image frames, and a computer system (14 or 20) for receiving such video images. The computer system periodically generates a background image of the scene from multiple successive image frames (23) and extracts features in the background image (26), and extracts features for each new image frame received from the camera (28). For each new image frames, the new image frame and the last periodically generated background image are correlated at common locations (parts or regions) associated with the features extracted from the last periodically generated background image and features of the new image frame (30), to determine non-correlated features in the new image frame with respect to the last periodically generated background image (31). If the number and/or percentage of non-correlated features are sufficiently high, and/or the spatial distribution of non-correlated features is sufficiently low, the image frame is determined to have an invalid background (32, 33). When multiple successive frames are determined as having invalid backgrounds, the camera (20) represents an invalid camera.