Video Analysis Engine for Sensory Effect Extraction
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Solution Overview
Problem
The production of sensory media, such as 4D movies, is hindered by high production costs and time due to the need for repetitive manual viewing and analysis, and existing automated techniques fail to provide satisfactory sensory effects as they do not consider context information.
Innovation Solution
A neural network learning model-based sensory effect information providing apparatus and method that extracts sensory effect information by analyzing videos, using a video analysis engine to separate frames, extract feature points, and associate sensory effects with objects or events, thereby generating sensory information for reproduction apparatuses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual viewing and analysis method is used to extract sensory effect information, then the accuracy and quality of sensory effects can be ensured, but the production time and cost increase significantly
Solution Approach 1:
The patent replaces the manual mechanical viewing and analysis process with an automated video analysis engine that uses neural networks and deep learning algorithms to extract sensory effect information from video frames, thereby eliminating the time-consuming manual process while maintaining extraction accuracy
Solution Approach 2:
The system enables self-service by allowing the video analysis engine to automatically extract sensory effect information without requiring manual intervention, using trained neural network models to perform the analysis independently and generate sensory effect metadata
2Extent of automation
If simple image analysis based on screen switching is used, then the automation level increases, but the quality of sensory effects deteriorates due to lack of context information analysis
Solution Approach 1:
The patent transitions from simple screen switching detection to multi-dimensional analysis by extracting features from video frames including color histograms, motion vectors, and contextual information, thereby analyzing sensory effects across multiple dimensions rather than just temporal transitions
Solution Approach 2:
The system performs preliminary action by pre-training neural network models with large amounts of video data and sensory effect information before actual sensory effect extraction, enabling the automated system to understand context and generate high-quality sensory effects without manual intervention
Data Source
AI summary
Disclosed is a sensory information providing apparatus. The sensory information providing apparatus may comprise a learning model database storing a plurality of learning models related to sensory effect information with respect to a plurality of videos; and a video analysis engine generating the plurality of learning models by extracting sensory effect association information by analyzing the plurality of videos and sensory effect meta information of the plurality of videos, and extracting sensory information corresponding to an input video stream by analyzing the input video stream based on the plurality of learning model.