Adaptive Threshold Moving Object Detection in Video Sequences
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Solution Overview
Problem
Existing methods for detecting moving objects in video image sequences, particularly in scenarios with changing camera angles or lighting conditions, suffer from inaccuracies and high false alarm rates due to image registration issues and temporal changes.
Innovation Solution
An adaptive image change threshold method is implemented, where initial image change points are determined and then revised based on quality criteria, allowing for iterative adjustments to improve robustness and reduce false alarms by selecting prominent feature points and displacement vectors for accurate transformation and difference image analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image registration is performed to compensate for camera movement and viewing angle changes, then the detection accuracy is improved, but significant inaccuracies still occur during the transformation process
Solution Approach 1:
The patent implements an iterative feedback mechanism where the image change threshold is continuously adjusted based on the distribution of image change points. The threshold determination unit repeatedly refines the threshold until the number of image change points falls within the expected range, creating a closed-loop system that automatically corrects for registration inaccuracies and adapts to varying image conditions.
Solution Approach 2:
The patent transforms the static image change threshold into a dynamic parameter that automatically adapts to different imaging conditions. By making the threshold flexible and adjustable based on actual image content and change point distribution, the system can respond to varying lighting, camera movement, and scene complexity without requiring manual recalibration.
2Ease of operation
If a fixed image change threshold is used to identify image change points, then the processing is simple, but the false alarm rate increases under varying lighting and recording conditions
Solution Approach 1:
The patent converts the fixed threshold into a dynamic adaptive threshold that automatically adjusts to different imaging conditions. The threshold determination unit calculates and refines the threshold based on the actual distribution of image change points, ensuring optimal detection performance across varying lighting, contrast, and scene conditions without sacrificing processing efficiency.
Solution Approach 2:
The system performs self-calibration by automatically determining and refining the image change threshold based on the image content itself. The threshold adjustment process is autonomous, using the distribution of image change points to self-correct and optimize detection parameters without external intervention, thereby maintaining both simplicity and reliability.
3Measurement precision
If the image change threshold is lowered to detect more subtle changes, then more moving objects are detected, but the number of false alarms increases
Solution Approach 1:
The patent uses feedback control to dynamically adjust the threshold based on the number and distribution of detected image change points. When too many change points are detected (indicating potential false alarms), the threshold is automatically raised; when too few are detected (missing actual objects), the threshold is lowered. This closed-loop mechanism maintains optimal sensitivity while minimizing false alarms.
Solution Approach 2:
The patent dynamically changes the image change threshold parameter based on the detected image content and change point distribution. By adjusting this critical parameter adaptively rather than using a fixed value, the system optimizes the balance between detection sensitivity and false alarm rate for each specific imaging scenario.
4Reliability
If multiple iterations of threshold adjustment are performed to improve detection quality, then the false alarm rate is reduced, but the processing time increases
Solution Approach 1:
The patent implements a stopping criterion that prevents excessive iterations by terminating the threshold adjustment process when the number of image change points falls within the expected range. This ensures that sufficient iterations are performed to achieve reliable detection while avoiding unnecessary additional iterations that would only increase processing time without improving detection quality.
Solution Approach 2:
The iterative threshold adjustment process incorporates feedback-based termination conditions that automatically stop iterations when detection quality reaches an acceptable level. The system monitors the number of image change points and halts the refinement process when this count falls within the expected range, optimizing the trade-off between detection reliability and processing efficiency.
Data Source
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AI summary
The method involves establishing an image variation threshold value and determining image variation points in difference image with an absolute image brightness value that exceeds the image variation threshold value. The quality of image variation points Is analyzed based on at least one predetermined quality criterion. The image variation points are established as distinctive image variation points with different image variation threshold value if the quality criterion is met. An independent claim is included for a device for the detection of moving objects in a video Image sequence.