Foreground Background Separation Scene Model Adaptation
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Solution Overview
Problem
Existing video processing techniques struggle to accurately separate foreground objects from background in dynamic scenes, particularly when faced with rapid and prolonged changes, leading to false detection issues due to their inability to adapt effectively to complex change-reversion scenarios.
Innovation Solution
A multi-modal scene model is employed, where each visual element is associated with multiple data sets, allowing for different learning rates and update methods to be applied, enabling better adaptation to changes and reducing false detections by using a dual approach with high and low learning rates for visual data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing background modeling techniques are used, then gradual and slow scene changes are handled well, but rapid and large scene changes result in false foreground detection
Solution Approach 1:
The patent implements dynamic adaptation by switching between two background modeling approaches: a first background model for gradual changes and a second background model for rapid changes. The system dynamically selects which model to update based on detecting the type of scene change, allowing it to adapt its behavior to match the current scene dynamics rather than using a fixed modeling approach
Solution Approach 2:
The patent changes the parameters of the background model based on the type of scene change detected. When rapid changes are detected, the system switches to a second background model with different update characteristics (higher learning rate or different update frequency) to capture the rapid transitions, whereas gradual changes use a first background model with slower adaptation parameters
2Reliability
If existing background modeling techniques are used, then large and fast scene changes are detected, but sudden reversion to steady state cannot be handled
Solution Approach 1:
The system dynamically switches between background models based on the duration and nature of scene changes. When a prolonged large change is detected, the system activates the second background model designed to handle extended transitions. Upon detecting reversion to steady state, the system can switch back to the first background model, allowing it to handle the full lifecycle of change-reversion scenarios
Solution Approach 2:
The patent employs periodic evaluation of scene change characteristics to determine which background model to use. The system continuously monitors for changes in change patterns, allowing it to detect when prolonged changes have occurred and when reversion has happened, switching models periodically based on these evaluations rather than using a single static approach
3Reliability
If existing background modeling techniques are used, then sudden reversion to steady state is detected, but large and fast changes cannot be handled
Solution Approach 1:
The system uses dynamic model selection where the second background model is specifically designed with higher adaptation speed to handle rapid changes and sudden reversions. The first background model maintains slower, more stable adaptation for normal conditions. The system dynamically selects which model to update based on the detected change characteristics, optimizing speed for rapid changes while maintaining stability for normal operation
Data Source
AI summary
A system and method for performing foreground/background separation on an input image. The method identifies a corresponding model visual element in a scene model associated with the input image, the model visual element being associated with a set of element models, each element model including a plurality of visual data sets. Select an element model from the set of element models, dependent upon a visual distance between the input visual element and a visual data set of the selected element model satisfying a predetermined criterion. The method classifies the input visual element as one of foreground and background, dependent upon the selected element model, and then updates each visual data set in the selected element model, dependent upon the input visual element and at least first and second different methods of updating a visual data set.