Surveillance Camera Abnormality Detection via Frame Comparison
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Solution Overview
Problem
Conventional surveillance cameras often fail to accurately detect abnormalities such as blocked, redirected, sprayed, or defocused images, leading to delayed and inadequate maintenance, compromising security and property protection.
Innovation Solution
A method that involves obtaining video frames from surveillance cameras, comparing them to a reference frame to determine normal or abnormal status, and issuing maintenance notifications for specific abnormal properties, thereby facilitating timely and targeted maintenance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If operators manually determine video quality using their eyes, then the surveillance system can operate with simple equipment, but the detection accuracy and maintenance timing are delayed
Solution Approach 1:
The surveillance camera system performs self-diagnosis by automatically comparing its captured video frames against reference frames to detect abnormalities such as blocking, spraying, defocusing, and redirection. This self-service mechanism eliminates the need for manual visual inspection by operators, enabling timely detection and maintenance without human intervention.
Solution Approach 2:
The patent replaces the mechanical/visual inspection method (operators using their eyes) with an automated image processing system that compares video frames using computational algorithms. This substitution enables precise, objective, and timely abnormality detection without relying on human visual assessment.
2Ease of repair
If general maintenance is performed without identifying specific abnormalities, then maintenance procedures are simpler, but maintenance quality and effectiveness are reduced
Solution Approach 1:
The patent segments the maintenance process by first detecting the specific type of abnormality (blocking, spraying, defocusing, or redirection) through frame comparison, then directing targeted maintenance actions for each abnormality type. This segmentation enables precise maintenance rather than generic procedures, improving maintenance quality while maintaining procedural clarity.
Data Source
AI summary
A method of detecting whether a surveillance camera is abnormal or abnormal includes: obtaining a plurality of video frames from a video captured by the surveillance camera; sequentially comparing each of the plurality of video frames with a reference video frame; determining that the surveillance camera is abnormal when a sequence of video frame detection results indicates that not all of the plurality of video frames are normal video frames; issuing a maintenance notification of the surveillance camera when it is determined that the surveillance camera is abnormal with the particular one of the plurality of abnormal properties, thereby facilitating a maintenance procedure to be performed on the surveillance camera to remove the particular one of the plurality of abnormal properties.