Video Surveillance False Alarm Reduction via Median Pixel Analysis
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Solution Overview
Problem
Video surveillance systems face accuracy issues due to fast-moving objects caused by environmental conditions such as rain, snow, leaves, and insects, leading to false alarms.
Innovation Solution
A method that involves capturing a series of images over a time period, identifying corresponding pixels, determining the median pixel value across all images, and generating a resulting image to mitigate the effects of fast-moving objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video surveillance systems capture images continuously to detect intruders, then detection capability is improved, but false alarms increase due to fast moving objects like rain, snow, leaves and insects
Solution Approach 1:
The system performs preliminary action by capturing multiple images before making a detection decision. Instead of reacting to a single image that may contain fast-moving objects, the system proactively captures a sequence of images and processes them together to identify and eliminate false alarms caused by environmental factors like rain, snow, leaves and insects.
Solution Approach 2:
The system creates multiple copies (images) of the same scene captured at different time points. By comparing these duplicate images, the system can identify pixels that change between frames (fast-moving objects) versus pixels that remain stable (actual intruders or environmental background), thereby filtering out false alarms while maintaining detection accuracy.
2Object-generated harmful factors
If the system processes multiple images to reduce false alarms, then false alarm rate decreases, but processing time and computational complexity increase
Solution Approach 1:
The system segments the image processing task by analyzing individual pixels independently across multiple images. Instead of processing entire images as single units, the system divides the problem into pixel-level operations, comparing corresponding pixels across frames to identify changes. This segmentation enables efficient parallel processing and reduces overall computational complexity.
Solution Approach 2:
The system changes the parameter being analyzed from entire image content to individual pixel intensity values. By focusing on pixel-level intensity changes rather than complex image feature analysis, the system reduces processing requirements while maintaining the ability to distinguish fast-moving objects from stationary intruders, thereby reducing false alarms without proportionally increasing processing time.
Data Source
AI summary
The present application describes a system and method for mitigating false alarms caused by fast moving objects in a scene.