Camera System Metadata Extraction for Video Highlight Generation
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Solution Overview
Problem
Manual searching through raw videos to identify the best scenes is time-consuming, and automated video processing is resource-intensive, especially with high-resolution data, making it difficult to improve the video editing experience.
Innovation Solution
A camera system that captures metadata such as motion, acceleration, and GPS data to automatically identify best scenes and generate video summaries, using a video server to analyze this metadata and create summaries without relying on manual curation or image processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated video processing is used to identify best scenes, then productivity is improved, but use of energy increases due to resource-intensive processing of high-resolution video data
Solution Approach 1:
The patent extracts only the essential metadata (motion data, orientation data, location data) from the video content, processing this extracted information instead of the full high-resolution video data. This extraction approach enables automated best scene identification while significantly reducing computational resource requirements and energy consumption.
Solution Approach 2:
The patent introduces metadata as an intermediary layer between the raw video data and the processing system. By processing metadata (which is much smaller and easier to analyze) rather than the full video content, the system achieves automated video analysis with reduced resource intensity while maintaining the ability to identify best scenes effectively.
2Use of energy by moving object
If manual searching through raw videos is used to identify best scenes, then use of energy is reduced, but loss of time increases due to time-consuming scrubbing process
Solution Approach 1:
The system enables self-service automated video analysis by processing metadata that is automatically captured during video recording. The camera system itself generates and stores the metadata (motion, orientation, location data) during capture, which is then used to automatically identify best scenes without requiring manual intervention or intensive post-processing resources.
3Measurement precision
If full automated processing of high-resolution video data is performed, then measurement precision is improved for scene identification, but device complexity increases
Solution Approach 1:
The patent extracts and processes only the critical metadata elements (motion vectors, orientation changes, location coordinates) needed for scene identification. This selective extraction maintains measurement precision for identifying best scenes while avoiding the complexity of processing entire high-resolution video frames, thus reducing device complexity.
Solution Approach 2:
The patent changes the parameters being processed from full video frame data to condensed metadata parameters (motion data, orientation data, location data). This parameter transformation maintains the ability to accurately identify scenes of interest while significantly simplifying the processing system requirements and reducing device complexity.
Data Source
AI summary
Video and corresponding metadata is accessed. Events of interest within the video are identified based on the corresponding metadata, and best scenes are identified based on the identified events of interest. Events of interest can be tagged within the video based on, for instance, user input, audio signals, motion vectors, and metadata corresponding to the video. A camera system can process video data based on the events of interest tagged within the video before outputting the video data. For instance, video scenes associated with tagged events of interest can be combined to form a video highlight clip. Likewise, portions of video tagged with events of interest can be encoded or stored at a higher resolution or frame rate than other portions of the video.