Foreground Background Separation Using Codebook Clustering
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Solution Overview
Problem
Conventional foreground and background separation technologies face challenges in modeling rapid background changes with a small number of Gaussians, leading to incorrect classification of slowly changing backgrounds as foreground, and are inefficient due to the creation of excessive models, resulting in prolonged processing times.
Innovation Solution
An apparatus and method that create a code book with a suitable number of code words using K-means clustering for pixel-based background modeling, where code words are sets of sample data with similar color tones, and the separation is performed by determining the distance and brightness of samples within these code words, using Euclidean distance and normal distribution probability thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the number of Gaussians is increased to model rapid background changes, then the ability to model rapid changes is improved, but slowly changing backgrounds are incorrectly determined as foreground
Solution Approach 1:
The patent changes the fundamental parameter from using Gaussian distribution parameters (mean, variance) to using codebook-based color quantization parameters. By representing background colors as discrete codebook entries rather than continuous Gaussian distributions, the system can accurately model rapid background changes without creating false foreground detections, as each codebook entry represents a specific color region with defined boundaries.
Solution Approach 2:
The patent segments the continuous color space into discrete codebook entries, where each entry represents a specific color region. This segmentation allows the background model to handle rapid changes by switching between discrete color regions rather than adjusting continuous Gaussian parameters, thereby improving both adaptability to rapid changes and reliability in preventing false foreground detection.
2Measurement precision
If the number of Gaussians is increased to improve background modeling accuracy, then the modeling precision is improved, but the device complexity and processing time increase
Solution Approach 1:
The patent extracts only the essential color information from the continuous color space by creating a discrete codebook of representative color values. Instead of maintaining multiple Gaussian models to cover all possible color variations, the system extracts key color regions into codebook entries, significantly reducing the number of models needed while maintaining background modeling accuracy.
Solution Approach 2:
The patent changes from using continuous Gaussian parameters (which require multiple overlapping models to cover color variations) to discrete codebook indices. This parameter change reduces complexity because the system only needs to store and search discrete codebook entries rather than continuous Gaussian distributions, thereby reducing both device complexity and processing time while maintaining accuracy.
3Reliability
If conventional background modeling is used to separate foreground and background, then the separation can be performed, but excessive time is consumed due to searching through many models
Solution Approach 1:
The patent performs preliminary action by pre-computing and storing a codebook of representative background color values before the actual foreground/background separation process. This pre-computed codebook serves as a ready-reference lookup table, eliminating the need to search through multiple Gaussian models during real-time separation, thereby significantly reducing processing time while maintaining separation reliability.
Solution Approach 2:
The patent creates a simplified copy of the background color space in the form of a discrete codebook, which captures the essential color characteristics without the complexity of full Gaussian models. This codebook copy allows for rapid comparison and classification during separation operations, reducing the time required to search through models while preserving the ability to accurately separate foreground and background.
Data Source
AI summary
Disclosed herein is an apparatus and method for separating a foreground and a background. The apparatus includes a background model creation unit for creating a code book including a plurality of code words in order to separate the foreground and the background, and a foreground/background separation unit for separating the foreground and the background using the created code book. The method includes the steps of creating a code book including a plurality of code words in order to separate the foreground and the background, rearranging the cord words of the created code book on the basis of the number of sample data that belong to each of the code words, and separating the foreground and the background using the code book.