Image Detection History Smoothing for Face Recognition
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Solution Overview
Problem
Existing image-taking systems are inefficient in detecting predetermined objects, such as faces, from images due to reliance on past features without considering the frequency and reliability of detection results.
Innovation Solution
An image-taking system that stores detection history and uses it to determine the presence of an object by referencing past and new detection results, allowing for efficient detection even if the object is not detected in the newest image, as long as it meets predetermined conditions, and smoothes the position and size of detected objects for improved handling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the objective body is detected only in the newest image, then the detection is simple and fast, but the detection reliability is low because the object may be missed due to temporary undetection
Solution Approach 1:
The system performs preliminary detection actions by storing detection results from past images before the newest image is processed. When the newest image is analyzed, the system refers to the stored detection history to confirm object presence, thereby improving reliability without requiring complex real-time analysis of every parameter from scratch.
Solution Approach 2:
The system implements feedback by continuously referring to stored detection history when determining object presence in the newest image. The determination process uses feedback from past detection results (whether the object was detected previously, its position, and size) to make a more reliable determination about the current image, reducing false negatives.
2Productivity
If the detection history is stored and referenced, then the detection efficiency is improved, but the memory usage and data processing load increase
Solution Approach 1:
The system extracts only the essential detection information needed for efficient determination - specifically whether the object was detected, its position, and its size - from the full image data and stores only these key parameters in the detection history. This extraction approach improves detection efficiency while minimizing the quantity of stored data.
Solution Approach 2:
The system applies local quality by storing detailed detection information (position, size, detection status) only for the specific region of interest where the objective body is located, rather than storing or processing entire image data. This localized storage approach optimizes the balance between detection efficiency and data storage requirements.
3Measurement precision
If the object position and size are smoothed from detection history, then the detection accuracy is improved, but the processing time increases
Solution Approach 1:
The system performs partial smoothing by applying smoothing algorithms only to the specific parameters (position and size) that need refinement, rather than processing all detection data comprehensively. This partial action approach improves measurement precision for critical parameters while minimizing the additional processing time required.
Solution Approach 2:
The smoothing of position and size information is performed as a preliminary step using readily available detection history data before final determination is made. By preparing smoothed reference values in advance from historical data, the system reduces the computational burden during real-time detection, thereby limiting the increase in processing time.
Data Source
AI summary
In an image taking system such as digital camera, especially when a predetermined objective body such as a face is included, an objective body detecting portion detects a predetermined objective body from an image taken by the image taking system. A storage portion stores a detection history including a past result of detection of the objective body and a newest result of detection of the objective body. A determination portion refers to the detection history and determines whether the objective body is to be handled as detected in the image obtained newest.