Surveillance Image Analysis Using Feature Block Training
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Solution Overview
Problem
Conventional surveillance apparatuses struggle to obtain clear images for recognition in scenarios with long distances, low light, or fast-moving objects due to hardware limitations.
Innovation Solution
An image analysis method where a surveillance apparatus, equipped with an image receiver and an operation processor, obtains multiple image frames with varying feature block definitions. The operation processor selects feature blocks from these frames as training samples for an image analysis model when certain conditions are met.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional surveillance apparatus uses hardware-based image capture, then image acquisition is simple, but image definition and recognition accuracy deteriorate under long distance, low light, or fast-moving conditions
Solution Approach 1:
The patent creates a virtual copy of the image analysis function through software modeling. Instead of relying solely on hardware quality, the system captures multiple low-definition images and uses an AI model to generate a high-definition virtual representation, effectively copying the desired image quality through computational means rather than physical hardware improvements
Solution Approach 2:
The system changes the parameter of image definition from a fixed hardware limitation to a variable that can be improved through processing. By analyzing multiple frames with different definitions and using AI models to synthesize high-definition features, the system transforms low-definition input parameters into high-definition output parameters
2Measurement precision
If the surveillance apparatus processes multiple image frames with different definitions, then recognition accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by capturing and storing multiple image frames in advance before the actual recognition task. These pre-acquired frames with varying definitions serve as training data that prepares the AI model for accurate recognition, allowing the model to make rapid accurate judgments without processing every frame in real-time during the actual recognition event
Solution Approach 2:
The system uses feedback from analyzing multiple frames with different definitions to train and improve the AI model. The model learns from the variations in definition across frames and uses this feedback to develop more accurate recognition capabilities, continuously improving its performance based on the analyzed data
3Measurement precision
If the surveillance apparatus uses AI model training with feature blocks, then image analysis accuracy improves, but device complexity and processing requirements increase
Solution Approach 1:
The patent segments the image into feature blocks and analyzes these segments separately. Instead of processing entire images, the system divides them into manageable feature units that can be individually analyzed and trained, reducing the complexity of processing while maintaining overall accuracy through the aggregation of segmented analysis results
Data Source
AI summary
An image analysis method performed in a surveillance apparatus having an image receiver and a operation processor is provided. The image analysis method includes the operation processor controlling the image receiver to obtain a plurality of image frames including a first image frame and a second image frame, wherein a definition of a first feature block of the first image frame is different from a definition of a first feature block of the second image frame; and the operation processor taking the first feature block of the first image frame and the first feature block of the second image frame as training samples for training an image analysis model when the operation processor determines the first feature block of the first image frame meets a preset condition. Besides, a related surveillance apparatus is also provided.