Event Detection Training System Using Keyword-Image Retrieval
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Solution Overview
Problem
Existing event detection systems, such as CCTV and car black boxes, struggle to accurately specify event details and improve detection reliability over time, and are inflexible in adapting to new events after initial installation.
Innovation Solution
A method and apparatus for training a learning system using user-selected keywords to collect and process images, allowing for retraining with test and false images to enhance detection accuracy and adaptability, utilizing a combination of keyword and image databases with deep learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a simple event detection system is used, then the device complexity is reduced, but the measurement precision of event details deteriorates
Solution Approach 1:
The system is segmented into multiple functional modules: keyword collecting unit, related keyword collecting unit, search formula generating unit, image collecting unit, and training unit. Each module performs a specific task in the event detection process, allowing the complex functionality to be distributed across simpler, specialized components.
Solution Approach 2:
The patent introduces intermediate processing elements such as the search formula generating unit that transforms keywords into search queries, and the training unit that bridges raw images and the learning system. These intermediaries enable precise event detection without requiring direct complex connections between all components.
2Device complexity
If a fixed detection system is installed, then the device complexity is minimized, but the adaptability to new events deteriorates
Solution Approach 1:
The system incorporates dynamic retraining capabilities where the training unit can continuously update the learning system with new images and keywords. This allows the system to adapt to new events over time without requiring complete system replacement or complex reconfiguration.
Solution Approach 2:
The learning system performs self-improvement through automated retraining processes. The system can independently learn from new data collected by the image collecting unit, reducing the need for manual system reconfiguration and enabling automatic adaptation to new event types.
3Ease of operation
If traditional detection methods are used, then the ease of operation is maintained, but the reliability of event detection deteriorates
Solution Approach 1:
The system implements feedback loops where detection results are continuously evaluated and used to retrain the learning system. The training unit receives feedback from detection outcomes and adjusts the model accordingly, improving reliability while maintaining automated operation that does not increase user burden.
Solution Approach 2:
The patent replaces traditional mechanical or rule-based detection systems with a learning-based system that uses machine learning algorithms. This substitution improves detection reliability by enabling the system to learn complex event patterns automatically, while the automated training process maintains ease of operation.
Data Source
AI summary
The present disclosure relates to apparatus and method for training a learning system to detect event. The present disclosure provides apparatus and method for training a learning system that includes an event related keyword collecting unit that collects an event related keyword inputted by a user, a related keyword collecting unit that collects at least one related keyword from a word database by transmitting an event related keyword to the word database, an event related image collecting unit that collects at least one event related image that is related to a search formula from an image database by transmitting the search formula to the image database, and a training unit that trains a learning system by communicating with the learning system with a use of an event related image as training data. In consequence, it is capable of setting a specific event to be detected by an apparatus for training a learning system by a user after a system is installed, and it is possible to train a learning system sustainably and sufficiently regarding a specific event and achieve a high accuracy of training the learning system.