Video Event Prediction for Automated Terminal Response Timing
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Solution Overview
Problem
Conventional video learning algorithms are not applied to automatically analyze video to record timing points related to electronic terminals, requiring a labor-intensive manual process to measure response times.
Innovation Solution
A system and method that uses processing circuitry to automatically detect events in a video, predict future events, and calculate time stamps, superimpose annotations, and store the results, utilizing a neural network trained on previous video sequences for improved efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual frame-by-frame video analysis is used to measure response times, then measurement precision can be maintained, but productivity decreases significantly due to labor intensity
Solution Approach 1:
The patent replaces manual mechanical frame-by-frame analysis with an automated computer-based video analysis system that uses image processing and machine learning algorithms to detect events and measure response times automatically, eliminating human labor while maintaining measurement precision
Solution Approach 2:
The video analysis system performs self-service by automatically detecting events, capturing timestamps, and calculating response times without human intervention, enabling the system to process multiple videos independently and significantly increasing productivity
2Productivity
If automated video analysis is implemented, then productivity increases, but device complexity increases due to advanced algorithms and processing requirements
Solution Approach 1:
The patent introduces intermediate components such as event detection modules, timestamp capture mechanisms, and machine learning models that act as mediators between the raw video data and the final response time measurements, managing complexity through modular architecture
Solution Approach 2:
The video analysis system is segmented into distinct functional modules including event detection, timestamp capture, and response time calculation, allowing each component to be developed and optimized independently while reducing overall system complexity
3Ease of operation
If conventional video learning algorithms are not applied, then ease of operation is maintained through simple manual processes, but loss of time increases due to manual labor requirements
Solution Approach 1:
The system performs preliminary actions by pre-training machine learning models on video data and pre-configuring event detection parameters before actual video analysis, enabling rapid automated processing without complex manual setup during operation
Data Source
AI summary
Systems and methods of performing video analysis related to a video of an electronic terminal. In one exemplary embodiment, a method is performed by an electronic device that includes processing circuitry. The method may include causing a display to play a video that shows an electronic terminal, automatically detecting a captured event related to the electronic terminal in the video, capturing first time stamp information corresponding to a time point in the video that the captured event occurs in the video, and predicting a future event associated with the captured event related to the electronic terminal in the video. The method may also include capturing and outputting second time stamp information corresponding to a time point in the video that the predicted future event is detected to have occurred in the video.