Image Processing Apparatus Estimation Area Setting
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Solution Overview
Problem
Existing people counting systems face challenges in accurately setting estimation areas for image processing, especially when a sufficient number of objects are not visible in the captured image, leading to suboptimal size settings for regression-based estimation methods.
Innovation Solution
An image processing apparatus that includes a detection unit for identifying objects, a holding unit for storing object information, a determination unit to assess the number of detected objects, and a setting unit that sets estimation areas based on the object information, ensuring the ratio of area to object size matches training data, enhancing detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a user manually sets estimation areas on images by confirming object sizes, then the estimation area size can be adjusted to match training data conditions, but the process becomes time-consuming and may not achieve sufficient sample sizes when objects are sparse in captured images
Solution Approach 1:
The system performs automatic estimation area setting using detection results from multiple images. The control unit automatically calculates average object sizes and determines estimation area sizes without requiring manual user intervention, making the system self-sufficient in configuring parameters based on detected object characteristics.
Solution Approach 2:
The system accumulates detection results from multiple images in advance and calculates average object sizes before final estimation area setting. This preliminary accumulation of data allows the system to establish accurate estimation areas based on statistical averages, improving both accuracy and efficiency.
2Quantity of substance
If the number of objects in captured images is insufficient, then it becomes difficult to determine appropriate estimation area sizes proportional to object sizes, but increasing image capture frequency or area may not be feasible in all scenarios
Solution Approach 1:
The system merges detection results from multiple captured images by accumulating detection data across images. The control unit combines object detection information from several images to calculate average object sizes, effectively increasing the sample size and statistical reliability even when individual images contain few objects.
Solution Approach 2:
The system performs preliminary accumulation of detection data from multiple images before conducting the final estimation. This advance data gathering ensures sufficient sample size for accurate average calculation, resolving the issue of insufficient objects in single images.
3Productivity
If estimation areas are set without sufficient object samples, then the processing speed can be maintained, but the accuracy of object number estimation deteriorates due to inappropriate area sizes
Solution Approach 1:
The system performs preliminary accumulation of detection results from multiple images and calculates average object sizes before the final estimation process. This advance preparation ensures that estimation areas are set with accurate reference data, improving estimation accuracy without significantly impacting processing speed.
Solution Approach 2:
The system automatically configures estimation areas based on detected object characteristics without requiring manual adjustment. The control unit self-adjusts estimation area sizes based on calculated average object sizes, maintaining processing efficiency while improving accuracy through data-driven parameter selection.
Data Source
AI summary
An image processing apparatus includes a detection unit that executes detection processing for detecting a particular object in an image, a holding unit that holds object information indicating a position and a size of the particular object on the image, a determination unit that determines whether a number of times a particular object is detected in the detection processing on one or more images reaches a predetermined value, a first setting unit that, when the number of times a particular object is detected in the detection processing on the one or more images is determined to reach the value, sets estimation areas on an image based on the object information obtained by the detection processing on the one or more images, and an estimation unit that executes estimation processing for estimating a number of the particular objects in the estimation areas.