Reference Outline Adjustment for Moving Object Counting
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Solution Overview
Problem
Current image processing systems face difficulties in accurately analyzing and counting moving objects in captured images, particularly due to variations in image capturing conditions such as height, angle, and lens distortion.
Innovation Solution
An image processing apparatus with a motion detection unit, object detection unit, outline generation unit, and calculation unit that adjusts reference outlines based on preset parameters, including height, image capturing angle, zoom ratio, and lens distortion characteristics, allowing users to select quantized parameters through a user interface for accurate object counting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed-size reference outlines are used for object detection, then the device complexity is reduced, but the measurement precision deteriorates due to variations in image capturing conditions
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing reference outline sizes for multiple circumstantial parameters (height, image capturing angle, zoom ratio, lens distortion) before actual object detection. The system provides a lookup table or pre-configured parameters that can be quickly selected based on current capturing conditions, avoiding real-time complex calculations while maintaining high measurement precision.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting the reference outline size based on detected circumstantial parameters. When image capturing conditions change (height, angle, zoom, distortion), the system automatically selects or calculates appropriate reference outline parameters from pre-defined sets, allowing accurate object counting across varying conditions without increasing device complexity.
2Measurement precision
If multiple circumstantial parameters are adjusted to improve object detection accuracy, then the measurement precision improves, but the ease of operation deteriorates
Solution Approach 1:
The patent applies self-service by implementing automatic detection and selection of appropriate reference outline parameters based on current image capturing conditions. The system autonomously identifies circumstantial parameters (height, angle, zoom, distortion) and selects the corresponding reference outline size without requiring manual user configuration, thereby maintaining high measurement precision while simplifying operation.
Solution Approach 2:
The system performs preliminary classification and organization of reference outline parameters according to various circumstantial conditions. By pre-grouping parameters based on height, angle, zoom ratio, and lens distortion characteristics, the system enables automatic selection through simple user input or environmental sensing, eliminating the need for users to manually adjust multiple complex parameters.
3Ease of operation
If quantized parameters are provided through user interface, then the ease of operation improves, but the manufacturing precision deteriorates due to parameter discretization
Solution Approach 1:
The patent implements parameter changes by providing multiple quantized levels of circumstantial parameters (e.g., height categories, angle ranges, zoom levels) that users can easily select. For each quantized parameter level, pre-calculated reference outline sizes are stored with high precision. This approach allows simple user interaction through discrete selections while maintaining accurate reference outline dimensions for each category.
Solution Approach 2:
The system performs preliminary calculation and storage of reference outline sizes for each quantized parameter combination. By pre-computing accurate reference dimensions for discrete parameter levels (e.g., 5 height levels, 4 angle levels), the system eliminates the need for real-time precise calculations while maintaining manufacturing precision through carefully pre-determined values.
Data Source
AI summary
Provided are an image processing apparatus and method for counting moving objects in an image, the apparatus including: a motion detection unit which detects motion in an image; an object detection unit which detects objects based on the motion detected by the motion detection unit; an outline generation unit which generates at least one reference outline of which a size is adjusted according to a preset parameter based on a location in the image; and a calculation unit which calculates a number of objects having substantially a same size as that of the at least one reference outline from among the objects detected by the object detection unit, wherein the preset parameter is adjusted according to at least one circumstantial parameter.