POV Video Processing Platform for Panoramic Sports View Generation
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Solution Overview
Problem
Current video and audio capture, processing, and delivery systems face challenges in providing a comprehensive, immersive point of view (POV) experience, particularly in capturing and processing multiple streams from various sources within a venue, such as a sports stadium, to create a unified and engaging view for audiences.
Innovation Solution
The system receives and processes multiple content streams, including video and telemetry data, to detect artifacts and ascertain vector motion data, mapping spatial relationships between content sources, and generating a 'Sport of View' by stitching together frames from wearable cameras to provide a panoramic and dynamic view of events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple content streams are processed and stitched together to create a panoramic Sport of View, then the immersiveness and comprehensiveness of the POV experience is improved, but the processing complexity and computational requirements increase
Solution Approach 1:
The system divides the overall video processing task into separate stages: receiving individual content streams from multiple sources, processing each stream independently to detect artifacts and extract metadata, and then stitching the processed streams together to create the final panoramic Sport of View. This segmentation allows complex processing to be managed in manageable portions.
Solution Approach 2:
The system performs preliminary processing on each content stream before stitching, including artifact detection, metadata extraction, and spatial relationship mapping. By preparing each stream in advance with detected artifacts and vector motion data, the final stitching operation becomes more efficient and the overall processing complexity is reduced.
2Reliability
If real-time processing and streaming of multiple high-quality video streams is performed, then viewer engagement and experience quality are enhanced, but the energy consumption and processing power requirements increase
Solution Approach 1:
The system extracts only the essential information from each video stream, such as detected artifacts, metadata, and vector motion data, rather than processing every pixel and detail. This selective extraction maintains video quality while significantly reducing the computational energy required for real-time processing and streaming.
3Loss of information
If advanced metadata analysis and predictive motion tracking are implemented, then valuable insights for coaches and analysts are provided, but the processing time and computational resources increase
Solution Approach 1:
The system performs metadata analysis and motion tracking in advance during the processing stage, before the final stitching and delivery. By completing these computationally intensive analytical tasks preliminarily, the system provides valuable insights for coaches and analysts without adding delays to the real-time streaming delivery.
Data Source
AI summary
Embodiments of the present disclosure may provide methods and systems enabled to perform the following stages: receiving a plurality of content streams; retrieving metadata associated with each of the plurality of content streams; processing the metadata to detect at least one target annotation within at least one target content stream; retrieving telemetry data associated with the at least one target content stream; processing the telemetry data and the metadata associated with a plurality of frames in the at least one target content stream to ascertain vector motion data; and mapping a spatial relationship associated with at least one capturing device associated with at least one target content source.