Representative Frame Extraction for Video Content Analysis
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Solution Overview
Problem
Existing representative frame extraction techniques fail to effectively capture the content of home videos, as they focus on specific subjects like faces, missing the context of where the image was shot, and result in redundant frames when evaluating per-frame statistical features.
Innovation Solution
An information processing apparatus that detects and tracks image patterns, splits moving image data into intervals based on the presence of face sequences, and uses different evaluation rules to extract representative frames from both subject and non-subject intervals, ensuring that both person and scenery images are represented.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If representative frames are selected by focusing upon a moving image interval in which persons and faces could be detected, then frames containing persons and faces are extracted, but frames including scenery or subjects that leave an impression where persons and faces could not be detected are not selected as representative frames
Solution Approach 1:
The patent divides the video processing into two distinct segments: one for detecting persons/faces and another for detecting scenery. By segmenting the detection tasks, the system can extract representative frames for both categories without one dominating the other, thus preventing loss of scenery information while maintaining precision in person detection
Solution Approach 2:
The patent creates a universal representative frame extraction system that handles multiple types of subjects (persons, faces, and scenery) through a unified framework. The system uses different detection methods for different subject types but integrates them into a single representative frame selection process, making the system multi-functional rather than specialized for only person detection
2Measurement precision
If a representative frame is selected by taking a specific subject (a face, for example) as the object of interest, then who appears in the image can be ascertained, but information as to where the image was shot is missing
Solution Approach 1:
The patent dynamically switches between different detection modes based on the content being analyzed. When persons or faces are detected, the system focuses on identifying them; when scenery is detected, the system shifts focus to capturing location and contextual information. This dynamic adaptation allows the system to preserve both subject identification and location information depending on what is present in each frame
3Measurement precision
If an evaluation value is obtained on a per-frame basis, then detailed evaluation of each frame is achieved, but in a case where the purpose is to ascertain the content of video in home video, a large number of similar frames become indices that are redundant
Solution Approach 1:
The patent extracts only the essential and distinctive features from each frame for evaluation purposes, rather than processing all frames in detail. By extracting key characteristics and comparing them to identify similar frames, the system reduces the quantity of redundant frames while maintaining precise evaluation of the unique content in each frame
Solution Approach 2:
The patent changes the evaluation parameters based on the type of content being analyzed. For person detection, it uses facial recognition parameters; for scenery, it uses visual feature parameters. By adapting parameters to the content type and using clustering to group similar frames, the system achieves precise evaluation while minimizing redundant indices
Data Source
AI summary
An information processing apparatus for extracting a more appropriate representative frame image from moving image data that includes a plurality of frames of image data arranged in a time series includes: an input unit configured to input moving image data; a detecting unit configured to detect a frame image, which includes an image similar to a prescribed image pattern; a tracking unit configured to detect a frame image, which includes an image similar to the image included in the detected frame image; a storage unit configured to store successive frame images that have been detected by the tracking unit; a splitting unit configured to split the moving image data into a plurality of time intervals; and an extracting unit configured to extract a representative frame image using different evaluation rules.