Luminance Adjustment for Video Background Modeling
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Solution Overview
Problem
Existing luminance compensation methods in video processing are inadequate for real-time background modeling and background-differencing applications, as they often result in false foreground detections due to global luminance changes, and are computationally intensive, making them unsuitable for embedded systems.
Innovation Solution
A method and apparatus that adjust luminance values in a video sequence by determining multiple scenarios and corresponding luminance compensation values, accumulating brightness and darkness counts, and selecting an adjusted set of luminance values based on calculated metrics to minimize false detections and reduce computational requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If traditional luminance compensation methods are used to maintain luminance stability, then luminance consistency is improved, but false foreground detections increase due to sensitivity to luminance changes
Solution Approach 1:
The patent changes the parameter representation from absolute luminance values to relative luminance changes. By detecting whether luminance increases or decreases and applying compensatory adjustments in the opposite direction, the system maintains luminance stability without creating false foreground detections, as it adapts to luminance trends rather than reacting to absolute value changes.
Solution Approach 2:
The patent implements a feedback mechanism where the luminance compensation value is continuously adjusted based on detected luminance changes. The system monitors luminance trends across frames and dynamically modifies compensation parameters to counteract detected changes, creating a closed-loop control system that maintains stability while avoiding false detections.
2Stability of the object's composition
If comprehensive luminance compensation is applied to all regions, then luminance consistency is improved, but computational complexity increases
Solution Approach 1:
The patent segments the image into multiple regions and processes luminance compensation independently for each region. By calculating luminance statistics and applying compensation separately to different regions, the system achieves comprehensive luminance consistency while reducing overall computational complexity through parallelizable, modular processing.
Solution Approach 2:
The patent applies luminance compensation selectively based on detected luminance changes rather than uniformly to all regions. By identifying regions affected by luminance changes and applying compensation only where needed, the system maintains luminance consistency while minimizing unnecessary computational operations.
3Productivity
If luminance compensation is applied in real-time, then processing speed is improved, but accuracy may be reduced due to simplified processing
Solution Approach 1:
The patent performs preliminary calculations of luminance statistics and trends before applying compensation. By pre-computing luminance changes and determining compensation parameters in advance, the system enables real-time processing without sacrificing accuracy, as the computationally intensive analysis is completed beforehand.
Solution Approach 2:
The patent changes from complex per-pixel luminance analysis to simplified regional luminance trend detection. By monitoring overall luminance increases or decreases and applying corresponding compensation, the system achieves real-time processing speed while maintaining sufficient accuracy for practical applications.
Data Source
AI summary
Disclosed are a method and apparatus for adjusting a set of luminance values associated with a set of visual elements in a current frame (310) of a video sequence for object detection (370). The method determines (410,430,450), for each of a plurality of scenarios (SO, SI, S2), a set of adjusted luminance values based on a corresponding luminance compensation value, and accumulates (460), for each scenario, a set of brightness counts and darkness counts of the current frame based on the set of adjusted luminance values. A metric (470) is calculated for each scenario based on the set of brightness counts and darkness counts and one of scenarios is selected based on the calculated metric. The method then selects (350) the adjusted luminance value associated with the selected scenario as an adjusted set of luminance values associated with the current frame of the video sequence.