Aerial Video Flight Paths for Audio-Synced Motion Capture
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Solution Overview
Problem
Current aerial image capturing devices, such as drones, face challenges in autonomously capturing video data of users engaging in activities like dancing, especially in synchronizing video with audio and maintaining accurate movement and orientation, while users struggle to control the device's positioning and audio capture simultaneously.
Innovation Solution
A machine-learning or artificial intelligence model is trained using video and flight path examples to set flight paths for aerial image capturing devices, allowing them to capture video data based on predefined parameters, including audio content and scene characteristics, and to synchronize video with audio, enabling independent control of zoom levels and movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual control methods are used for aerial image capturing devices, then users can control device positioning and audio capture, but the complexity of simultaneous control increases and synchronization accuracy deteriorates
Solution Approach 1:
The aerial image capturing device autonomously determines flight paths and captures video data without continuous manual intervention. The device uses trained models to automatically synchronize video capture with audio content, eliminating the need for users to manually coordinate positioning and audio capture while maintaining high synchronization accuracy.
Solution Approach 2:
Flight paths are pre-determined and stored before actual video capture. The system trains models using video data examples and flight path examples in advance, so that when capture begins, the device can automatically execute pre-planned trajectories synchronized with audio content without real-time manual control adjustments.
2Extent of automation
If automated flight path setting using trained models is implemented, then video capture synchronization and automation improve, but device complexity increases
Solution Approach 1:
A trained machine learning model acts as an intermediary between audio content and flight path determination. The model processes audio input and generates appropriate flight paths without requiring complex real-time control algorithms in the aerial device itself, distributing computational complexity to a separate training and inference system.
Solution Approach 2:
The system uses video data examples and flight path examples from social media applications or historical content to train models that replicate successful capture patterns. Instead of developing complex capture logic from scratch, the system copies and adapts proven flight paths and capture strategies from existing high-quality videos.
3Adaptability or versatility
If video capture is controlled independently of flight path settings, then capture flexibility improves, but coordination between movement and capture timing becomes more difficult
Solution Approach 1:
The control system is segmented into independent modules: flight path determination, video capture control, and audio synchronization. Each module operates independently but communicates through standardized interfaces, allowing flexible adjustment of capture parameters while maintaining reliable coordination through the structured interaction between segments.
Data Source
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AI summary
A method, apparatus and computer program is described comprising: setting a flight path for one or more aerial image capturing devices, based, at least in part, on one or more parameters, wherein the means for setting the respective flight path(s) comprises a model trained using at least one of: one or more video data examples or one or more flight path examples; and providing instructions to the respective aerial image capturing device(s) to capture video data of a first scene based on the respective flight path.