Image Processing Apparatus Edge Detection Inhibition
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Solution Overview
Problem
Conventional image processing techniques fail to accurately detect the passage of objects near the edges of a screen, leading to false negatives or false positives, especially when objects are small or moving rapidly, due to issues with barycenter tracking and detection line settings.
Innovation Solution
An image processing apparatus and method that sets a detection region and an inhibition region on the screen, preventing false detections by ensuring the detection line segment intersects with the object's motion vector within a defined area, and inhibiting detection near the screen edges to avoid false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If passage detection is performed using barycenter tracking, then detection simplicity is maintained, but detection accuracy deteriorates near screen edges
Solution Approach 1:
The patent applies different detection methods to different regions of the screen. In the central region, barycenter tracking is used for simplicity, while in the edge region, a different method based on object boundary detection and intersection calculation is used to maintain accuracy. This local differentiation resolves the contradiction by allowing simplicity where applicable and accuracy where necessary.
Solution Approach 2:
The screen is divided into a first region (central area) and a second region (edge area). This segmentation allows the system to apply different detection strategies to each region, using barycenter tracking in the first region and boundary-based detection in the second region, thereby maintaining both simplicity and accuracy across different areas.
2Area of stationary object
If detection line is set near screen ends, then coverage area is maximized, but false detection increases
Solution Approach 1:
Different detection criteria are applied to different regions. In the first region (central area), standard barycenter-based passage detection is used. In the second region (edge area), a more rigorous boundary-based detection method is used that calculates intersection between object boundaries and detection lines, reducing false detections while maintaining coverage.
Solution Approach 2:
The detection system is segmented into two operational modes corresponding to the two regions. The first region uses simplified detection logic, while the second region uses enhanced detection logic with additional verification steps, allowing the system to maintain high reliability even when detection lines are positioned near screen ends.
3Reliability
If object filtering by size is applied, then false detection is reduced, but detection of small objects deteriorates
Solution Approach 1:
Different detection thresholds and criteria are applied to different regions of the screen. In the first region, standard size-based filtering is used to reduce false detections. In the second region, the system uses boundary-based detection with adjusted parameters that are more sensitive to small objects, ensuring they are not missed while still maintaining reliability through the rigorous intersection calculation method.
Data Source
AI summary
An image processing apparatus includes a detection unit that detects that a center position of an area corresponding to at least a portion of an object moving within a display screen has moved across an object detection line segment set in the display screen. A setting unit sets an inhibition region, where the detection is inhibited, along a frame of the display screen to provide both (i) a non-inhibition region where the detection is not inhibited and (ii) the inhibition region, together with each other in the display screen, after setting the inhibition region. The non-inhibition region is a remaining region in the display screen, other than the inhibition region. The detection unit detects that the center position has moved across the object detection line segment set in the non-inhibition region.