Motion Object Image Merging for Cloud Identification Accuracy
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Solution Overview
Problem
Current cloud identification technologies face challenges with high-resolution image processing, leading to increased data transmission costs, computational complexity, and potential failure in real-time motion object identification due to network congestion and insufficient image integrity, resulting in repeated identification loops and increased processing time.
Innovation Solution
A motion image integration method and system that detects and merges partial motion region images to generate a motion object image, cropping the raw image to create a sub-image with a larger range, thereby enhancing image integrity and reducing unnecessary identification loops by maximizing pixel information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-resolution images are used for cloud identification, then identification accuracy is improved, but data transmission costs and computational complexity increase
Solution Approach 1:
The patent segments the image processing task by first detecting motion regions in the raw image, then cropping only those regions to generate motion object images. This segmentation approach allows the cloud server to process only relevant portions of the image at high resolution, rather than entire high-resolution images, thereby reducing computational complexity while maintaining identification accuracy for motion objects.
Solution Approach 2:
The patent extracts motion regions from the raw image using motion detection algorithms, then crops the raw image to generate sub-images containing only the motion objects. This extraction process removes unnecessary background and static elements, reducing the data volume that needs to be transmitted and processed while preserving the integrity of motion objects for accurate identification.
2Measurement precision
If high-resolution images are transmitted through the network, then identification accuracy is improved, but real-time processing fails due to network congestion and hardware resource reallocation
Solution Approach 1:
The patent extracts only the necessary motion object regions from raw images before transmission. By cropping the raw image to generate sub-images containing only motion objects, the data transmission bandwidth requirement is significantly reduced, allowing real-time processing to be maintained while still providing sufficient image quality for accurate cloud identification.
Solution Approach 2:
The patent applies partial action by transmitting only the essential portions of images (motion object regions) rather than complete high-resolution images. This partial transmission approach provides just enough image data for real-time motion object identification without the excessive bandwidth consumption that would prevent real-time processing.
3Loss of time
If only a small part of a motion object image is detected, then processing time is reduced, but identification fails due to insufficient image integrity
Solution Approach 1:
The patent merges multiple detected motion regions by calculating their union to generate a comprehensive motion object image. This merging process ensures that all parts of motion objects are captured in a single integrated image, providing complete image integrity necessary for reliable identification while maintaining efficient processing by working with consolidated rather than scattered region data.
Solution Approach 2:
The patent performs preliminary motion detection and region merging before cloud identification, generating complete motion object images in advance. This preliminary action ensures that when images are transmitted to the cloud server, they already contain complete motion object information, eliminating the need for repeated identification attempts and ensuring reliable first-time identification.
4Measurement precision
If the cloud server repeatedly tries to identify the same motion object, then identification accuracy may be improved, but processing time increases
Solution Approach 1:
The patent performs preliminary processing on the client side by detecting motion regions, merging them into complete motion object images, and cropping raw images to generate optimized sub-images before transmission. This preliminary action ensures that the cloud server receives complete, well-prepared motion object images that are ready for immediate identification, eliminating the need for repeated identification loops and reducing overall processing time while maintaining high identification accuracy.
Data Source
AI summary
A motion image integration method includes acquiring a raw image, detecting a first motion region image and a second motion region image by using a motion detector according to the raw image, merging the first motion region image with the second motion region image for generating a motion object image according to a relative position between the first motion region image and the second motion region image, and cropping the raw image to generate a sub-image corresponding to the motion object image according to the motion object image. A range of the motion object image is greater than or equal to a total range of the first motion region image and the second motion region image. Shapes of the first motion region image, the second motion region image, and the motion object image are polygonal shapes.