Edge-Based Motion Detection for Low-Complexity Video Recorders
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Solution Overview
Problem
Traditional motion detection technologies in video recorders face challenges such as false alarms due to ambient light changes, irrelevant movement, and image noise, particularly in small-sized, portable devices which lack the computational resources needed for real-time processing using optical flow methods, and are prone to missing relevant motion events.
Innovation Solution
A method and system that detect motion events by acquiring and processing images to identify edge pixels, generating edge images, and determining differences between these images, with thresholds to indicate motion events, reducing noise and computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If optical flow methods are used for motion detection, then detection accuracy is improved, but computational complexity increases making real-time processing difficult on portable devices
Solution Approach 1:
The patent segments the image processing task by focusing only on edge pixels rather than all pixels. Edge detection algorithms identify and extract only the boundary pixels that contain motion information, dividing the full image into edge regions and non-edge regions. This segmentation reduces the number of pixels requiring complex optical flow analysis, thereby lowering computational complexity while maintaining detection accuracy on portable devices
Solution Approach 2:
The patent applies different processing qualities to different regions of the image. Edge regions undergo full optical flow analysis with high computational detail, while non-edge regions use simplified processing or are excluded from analysis. This local differentiation ensures that computational resources are concentrated where motion information is most critical, resolving the contradiction between accuracy and complexity
2Device complexity
If pixel value-based methods are used for motion detection, then computational complexity is reduced, but false detections from light interference and noise increase
Solution Approach 1:
The patent extracts and removes the harmful factor of light interference by using edge detection to isolate only the boundary pixels that contain meaningful motion information. By taking out the edge components from the full image and analyzing only those pixels, the method eliminates false detections caused by ambient light changes affecting entire image regions, while maintaining low computational complexity
Solution Approach 2:
The patent introduces edge detection as an intermediary step between image capture and motion analysis. This intermediary process filters out noise and irrelevant pixel variations before the actual motion detection occurs, allowing simple pixel-value-based methods to achieve higher reliability by processing only the cleaned edge information rather than the raw full-image data
3Ease of operation
If background subtraction methods are used, then motion detection capability is provided, but false alarms occur due to sensitivity to ambient light changes
Solution Approach 1:
The patent segments the image into edge and non-edge regions, applying background subtraction only to edge pixels. Since ambient light changes typically affect entire image regions uniformly, restricting the analysis to edge boundaries where actual object contours exist reduces sensitivity to such interference, thereby reducing false alarms while maintaining the ease of operation of background subtraction methods
Data Source
AI summary
A system for detecting a motion event based on images. The system acquires a first image and a comparison image, and generates a first edge image and a comparison edge image based on the first image and the comparison image. The system also determines the difference between the first edge image and the comparison edge image. The system indicates a motion event if the difference between the first edge image and the comparison edge image meets or exceeds a threshold.