Real-Time Performance Recording With ML-Driven Video Editing
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Solution Overview
Problem
Existing methods for recording and editing live performances require significant manual labor and often force a choice between static presentation or delayed broadcast, lacking dynamic camera movements in real-time display.
Innovation Solution
Utilizing machine learning models for video and audio analysis to generate editing commands that dynamically adjust camera movements and displays in real-time, incorporating convolutional neural networks (CNNs) for object detection and temporal analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual recording and editing techniques are used, then full creative control and polished final product are achieved, but significant manual labor and time consumption occur
Solution Approach 1:
The system enables self-service by automatically generating editing commands through machine learning models that analyze video and audio data. The automated camera movement system performs recording and editing functions without requiring manual intervention, allowing the system to serve itself while producing polished results.
Solution Approach 2:
The patent replaces manual mechanical editing operations with automated machine learning-based systems. Instead of manual camera adjustments and editing decisions, the system uses neural networks to automatically generate and execute editing commands, substituting human mechanical labor with intelligent automation.
2Adaptability or versatility
If real-time display of live performance is implemented, then dynamic camera movements and viewer engagement are improved, but manual control complexity increases
Solution Approach 1:
The system performs self-service by automatically determining camera movements and editing commands based on analyzed video and audio content. The machine learning models process the performance data and generate appropriate camera adjustments without requiring manual control, enabling dynamic adaptability while reducing operational complexity.
Solution Approach 2:
The system dynamically changes parameters such as camera position, angle, and zoom based on real-time analysis of the performance. By automatically adjusting these parameters according to the detected actions and audio characteristics, the system achieves adaptability without manual intervention.
3Ease of operation
If automated machine learning analysis is used, then manual effort is reduced, but computational resources and processing time are consumed
Solution Approach 1:
The system applies partial action by using machine learning models that process only the most relevant features from video and audio data. Instead of analyzing every pixel and sound wave in detail, the models focus on extracting key actions, objects, and audio characteristics, reducing computational requirements while maintaining effectiveness.
Data Source
AI summary
Systems and methods are described for creating dynamic displays of live events in real-time. A real-time display of a live performance will typically have a static presentation because there is no time for video editing to take place before the event is displayed to an end user. This static display may be considered less entertaining to viewers and cause them to view an event as uninteresting. The present disclosure enables a system to analyze the live event in real-time detect objects and people within the frame, associate sounds from the recording with the detected people and objects and generate video commands on the fly to implement on the video as it is displayed to an end user. This will enhance the presentation and make it more dynamic. There is a manual override for the generated commands if a user prefers to watch the more static display.