Foreground Background Image Determination Using Motion Level Analysis
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Solution Overview
Problem
Existing methods for determining foreground and background images in image sequences, such as registered and adaptive background methods, face challenges in accurately identifying foreground objects when they stop moving, leading to incorrect determinations and user inconvenience, especially in environments with changing light conditions.
Innovation Solution
A method that generates characteristic data for sub-region images within an interested region, classifies these images into groups based on their characteristics, calculates motion levels, and determines whether each group belongs to a foreground or background image based on motion levels and image quantities, allowing for accurate identification even when objects are stationary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If the determining method of adaptive background is used to automatically determine background images, then the ease of operation is improved, but the measurement precision deteriorates when foreground objects stop moving
Solution Approach 1:
The patent divides the image into multiple blocks and calculates motion levels for each block separately. By segmenting the image processing into block-level operations, the system can detect motion in specific regions without being affected by stationary objects in other regions, thus maintaining precision even when foreground objects stop moving.
Solution Approach 2:
The patent introduces a new dimension of motion level calculation based on block matching algorithms. Instead of relying solely on brightness value distributions, the system adds motion detection in the spatial dimension by comparing pixel displacements between frames, enabling accurate foreground identification regardless of motion state.
2Device complexity
If the adaptive background method classifies images based on brightness value distribution, then the device complexity is reduced, but the reliability deteriorates when objects stop moving
Solution Approach 1:
The patent dynamically adjusts the determination criteria by calculating motion levels for each image group. The system transitions from a static brightness-based classification to a dynamic approach that considers motion characteristics, allowing reliable foreground detection whether objects are moving or stationary while maintaining relatively simple device architecture.
3Manufacturing precision
If registered background images are used to represent the background, then the manufacturing precision is improved, but the adaptability deteriorates when light conditions change
Solution Approach 1:
The patent enables the system to automatically adapt to changing light conditions by calculating motion levels and image group characteristics without requiring manual intervention. The system serves itself by dynamically determining background images based on motion analysis, eliminating the need for users to manually select or update registered background images under varying environmental conditions.
Data Source
AI summary
A method for determining a foreground image and a background image, the method includes the following steps, generating a characteristic data of each of N sub-region images of in an interested region of N parent images, classifying the N sub-region images to image groups of in M image groups according to the characteristic data of each of the N sub-region images, obtaining a motion level of each of the M image groups according to a motion area of in the N sub-region images, determining whether each the image group belongs to a background image group or a foreground image group according to each the motion level of each the image group and an image quantity of in each the image group. The method can correctly determine a foreground image and a background image, even a foreground object stops moving and stays in a viewable range of an image apparatus.