Video Frame Camera Position Recovery Using 3D Keypoint Tracks
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Solution Overview
Problem
Content files, such as videos, often lack information about camera position, making it difficult to determine the trajectory, focal point, or focus of the camera used to capture the frames.
Innovation Solution
A computing device analyzes video frames using enhanced structure from motion (SFM) to identify key points, generates keypoint tracks, and uses these tracks to recover camera position by projecting frames into a three-dimensional model, employing techniques like Perspective-n-Point (PnP) and triangulation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional video frame analysis is used, then processing speed is maintained, but camera position information cannot be recovered
Solution Approach 1:
The patent segments the video processing task into distinct stages: feature point detection in each frame, keypoint track generation across multiple frames, and camera position recovery from aggregated track data. This segmentation enables sophisticated SFM analysis while maintaining manageable processing complexity through modular operation.
Solution Approach 2:
The patent performs preliminary feature point detection and keypoint track generation across all video frames before executing the final camera position recovery calculation. By pre-processing and aggregating feature data from multiple frames in advance, the system prepares comprehensive input data that enables accurate camera position determination without requiring complex real-time processing during the recovery phase.
2Measurement precision
If camera position is determined without SFM techniques, then processing is simpler, but position accuracy is insufficient
Solution Approach 1:
The patent continuously detects feature points and generates keypoint tracks across all video frames in sequence, maintaining an ongoing accumulation of spatial information. This continuous processing approach ensures that camera position recovery is based on comprehensive data from the entire video sequence, significantly improving accuracy compared to single-frame analysis while distributing computational load over time.
Solution Approach 2:
The system performs preliminary feature extraction and track generation for all frames before the final camera position calculation. This pre-computation of feature data eliminates the need for repeated intensive processing during position recovery, reducing overall processing time while maintaining high accuracy through multi-frame SFM analysis.
3Manufacturing precision
If graphical features are inserted without accurate camera position, then insertion is simpler, but frame quality degrades
Solution Approach 1:
The patent replaces traditional manual or heuristic-based graphical feature insertion methods with an automated system that uses computer vision and SFM algorithms to automatically determine camera position. This substitution of mechanical/manual processes with computational methods enables precise graphical feature placement based on recovered camera trajectories, improving insertion precision while the automated nature manages the complexity of position recovery.
Data Source
AI summary
Camera positions can be recovered for video frames. Keypoint tracks can be generated for a first video frame and subsequent video frames. Metric depth can be determined for the video frames. An image databased can be generated using the keypoint tracks and the metric depth. The image database can include the video frames. A first video frame having a highest number of keypoint tracks can be identified. A first projection into a three-dimensional model can be generated for the first video frame and a first matching video frame. A camera position can be generated using the first projection. A second video frame having a next-highest number of keypoint tracks can be identified. A second projection into a three-dimensional model can be generated for the second video frame and a second matching video frame. A camera position can be generated using the second projection.