Real-Time Video Stitching via Temporal Synchronization and Distortion Correction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Generating stitched video content from multiple overlapping and concurrently-captured video instances is challenging due to synchronization issues, distortion, and inefficiencies, particularly in real-time applications, where traditional methods struggle to produce accurate and seamless panoramic or 360° video without significant delays.
Innovation Solution
A video generation system that temporally synchronizes overlapping video instances, performs camera registration to determine distortion coefficients, undistorts and maps visual features, and stitches images onto a three-dimensional framework to create seamless panoramic video content, enabling efficient real-time generation of stitched video.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional sequential image stitching methods are applied to video, then panoramic video content can be captured, but synchronization issues and processing delays occur
Solution Approach 1:
The video stitching process is segmented into independent parallel operations: temporal synchronization of video frames, distortion coefficient calculation, undistortion processing, and feature mapping. Each segment can be processed independently or in parallel, reducing overall processing delay while maintaining stitching accuracy through systematic organization of operations.
Solution Approach 2:
Distortion coefficients are calculated preliminarily from the captured video instances before the actual stitching process. This preliminary action allows the undistortion step to use pre-computed parameters, significantly reducing real-time processing delays while ensuring accurate geometric correction of the video content.
2Area of stationary object
If multiple video capture devices are used to capture broader scenes, then panoramic coverage is improved, but synchronization and coordination complexity increases
Solution Approach 1:
Multiple video instances captured by different devices are merged through temporal synchronization that aligns frames from all devices to corresponding time points. This combining process integrates data from multiple sources into a unified panoramic view while managing synchronization complexity through systematic temporal alignment rather than complex coordinate transformations.
Solution Approach 2:
A central processing system acts as an intermediary that receives video streams from multiple capture devices, performs temporal synchronization, calculates distortion coefficients, and coordinates the stitching process. This intermediary manages the complexity of multi-device coordination by centralizing control and providing a unified processing pipeline.
3Area of stationary object
If wide-angle lenses are used to capture broader portions of scenes, then field of view is improved, but image distortion increases
Solution Approach 1:
The distortion characteristics of wide-angle lenses are characterized by calculating specific distortion coefficients from the captured video instances. These coefficients parameterize the distortion model, allowing the system to compensate for wide-angle distortion through mathematical transformation while retaining the advantage of broad field of view. The parameter change from raw distorted coordinates to corrected coordinates enables accurate panoramic stitching.
4Productivity
If real-time stitched video generation is implemented, then processing speed is improved, but computational complexity and resource requirements increase
Solution Approach 1:
Distortion coefficients are calculated preliminarily from the video instances before real-time stitching operations. This pre-computation separates complex computational tasks from real-time processing, allowing the main stitching pipeline to operate at high speed using pre-characterized distortion parameters, thus reducing real-time computational complexity while maintaining processing accuracy.
Solution Approach 2:
The computational process is segmented into offline/preliminary tasks (distortion coefficient calculation, calibration) and real-time tasks (temporal synchronization, frame undistortion, feature mapping). This segmentation allows computationally intensive operations to be performed when resources are available, while real-time operations use pre-computed data to achieve high processing speed with reduced complexity.
Data Source
AI summary
An exemplary video generation system (the “system”) receives a plurality of overlapping and concurrently-captured video instances from a plurality of video capture devices in a static configuration. The system temporally synchronizes concurrently-captured video data from each video instance to form a plurality of image sets and identifies a particular image set from the plurality of image sets. The system then performs a camera registration process on the particular image set in order to determine camera registration parameter data. Based on the camera registration parameter data determined using the camera registration process with respect to the particular image set, the system then performs a video stitching process on each of the image sets within the plurality of image sets to form stitched video content representative of a scene captured by the plurality of video capture devices in the static configuration. Corresponding methods and systems are also disclosed.