Image Analysis System Automating Video Feature Training Data
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Solution Overview
Problem
Preparing training data for machine learning models to detect features in videos is a burdensome process, requiring both video content and event information, which hinders efficient event detection.
Innovation Solution
An image analysis system comprising an information extraction section, a correct answer generation section, and a training section that extracts input regions and auxiliary information from video content, generates correct data, and trains a machine learning model using this data, allowing for easier detection of features in videos.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual preparation of training data including video content and event information is performed, then the machine learning model can detect features of events, but the preparation process becomes a huge burden and time-consuming
Solution Approach 1:
The system enables self-service by having the correct answer generation section automatically generate training data from video content without human intervention. The information extraction section extracts input regions and auxiliary information, which the correct answer generation section then processes to create training data, allowing the system to prepare its own training data autonomously and eliminating the time-consuming manual preparation process
Solution Approach 2:
The system performs preliminary action by extracting input regions and auxiliary information from video content before training the machine learning model. The correct answer generation section generates correct data from the extracted auxiliary information in advance, so that when training begins, the training data is already prepared and readily available, significantly reducing the overall time required for model development
2Measurement precision
If manual preparation of training data is performed, then accurate event information can be obtained, but the ease of implementation is reduced due to the burdensome process
Solution Approach 1:
The system achieves self-service by automatically generating training data through the correct answer generation section, which processes extracted auxiliary information to create accurate event data. This eliminates the need for manual data preparation while maintaining accuracy, as the system autonomously performs the entire data generation process without requiring human expertise or intervention
Solution Approach 2:
The system replaces the mechanical manual process of preparing training data with an automated information processing system. The information extraction section and correct answer generation section work together to automatically extract, process, and generate training data, substituting the manual mechanical process with an automated computational system that achieves the same accuracy more efficiently
Data Source
AI summary
Disclosed herein is an image analysis system including an information extraction section, a correct answer generation section, and a training section. The information extraction section extracts an input region and auxiliary information from video content, the input region being a portion of each of multiple images constituting the video content, the auxiliary information being information different from the input region. The correct answer generation section generates correct data from the extracted auxiliary information. The training section trains a machine learning model by use of training data including the input region and the correct data.


