Dynamic Background Model with Absorption Window for Video Analytics
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Solution Overview
Problem
Video analytics systems face challenges in accurately segmenting foreground and background elements in video frames due to noise, compression artifacts, and dynamic changes in lighting and scene conditions, leading to misclassification and increased computational complexity.
Innovation Solution
A computer-implemented method that updates a background model by classifying pixels as foreground or background using a pixel-based and context-based evaluation, incorporating absorption of foreground elements into the background model, with exponential weighting and morphological operations to refine the segmentation, allowing for efficient and accurate separation of scene elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a complex background model supporting multiple background states per pixel is created to handle dynamic scene changes, then the accuracy of foreground-background segmentation is improved, but the processing requirements and device complexity increase significantly
Solution Approach 1:
The patent segments the background model into multiple discrete states (e.g., daytime, nighttime, different lighting conditions) rather than maintaining a single complex multi-state model. Each state is handled independently with its own background model, simplifying the overall processing while maintaining accuracy for each specific condition.
Solution Approach 2:
The system dynamically selects which background state model to use based on current scene conditions (detected through foreground-background analysis and environmental sensors), allowing adaptability to changing conditions without maintaining all states simultaneously in memory, thus reducing processing complexity while preserving segmentation accuracy.
2Ease of manufacture
If traditional background modeling techniques are used to handle noise and compression artifacts, then the implementation is simple, but misclassification of pixels occurs under dynamic lighting conditions
Solution Approach 1:
The patent changes the parameters used for pixel classification by incorporating multiple factors beyond simple color distance, including temporal consistency across frames, spatial context from neighboring pixels, and environmental condition parameters. This allows accurate classification under dynamic lighting while maintaining implementation feasibility through incremental parameter additions.
Solution Approach 2:
The system introduces intermediate processing steps between raw pixel data and final classification, including noise filtering through temporal averaging, spatial context analysis using neighborhood pixel information, and environmental condition mediation. These intermediaries improve reliability without significantly complicating the overall implementation.
3Adaptability or versatility
If the background model is continuously updated to respond to gradual lighting changes, then the model remains current with scene conditions, but elements that should be background (e.g., parked cars, traffic lights) are incorrectly classified as foreground
Solution Approach 1:
The system performs preliminary classification of pixels as background or foreground before updating the background model. Pixels classified as background are updated in the model, while foreground pixels are held separate. This preliminary action prevents foreground elements (like parked cars) from being incorrectly incorporated into the background model, maintaining classification accuracy while allowing the model to adapt to genuine lighting changes.
Solution Approach 2:
The system uses feedback from foreground detection results to control background model updates. When foreground objects are detected, the system adjusts the update rate or threshold for background model changes, preventing premature incorporation of foreground elements. This feedback mechanism maintains adaptability to lighting changes while preserving classification precision.
4Speed
If processing is performed at full frame rate to achieve real-time segmentation, then the system responds dynamically to scene changes, but computational resources are excessively consumed
Solution Approach 1:
The patent implements periodic background model updates at a lower rate than full frame rate (e.g., updating every N frames or at a reduced frequency), while maintaining real-time foreground detection capability. This periodic approach reduces computational resource consumption for the intensive background modeling operations while preserving the system's ability to respond dynamically to scene changes through continuous lighter-weight foreground analysis.
Solution Approach 2:
The system dynamically adjusts processing intensity based on scene activity levels. During periods of high change or detected foreground activity, processing frequency increases to maintain real-time response. During stable periods with no significant changes, processing frequency decreases to conserve computational resources, optimizing the balance between response speed and energy consumption.
Data Source
AI summary
Techniques are disclosed for creating a background model of a scene using both a pixel based approach and a context based approach. The combined approach provides an effective technique for segmenting scene foreground from background in frames of a video stream. Further, this approach can scale to process large numbers of camera feeds simultaneously, e.g., using parallel processing architectures, while still generating an accurate background model. Further, using both a pixel based approach and context based approach ensures that the video analytics system can effectively and efficiently respond to changes in a scene, without overly increasing computational complexity. In addition, techniques are disclosed for updating the background model, from frame-to-frame, by absorbing foreground pixels into the background model via an absorption window, and dynamically updating background/foreground thresholds.