Automatic Camera Identification for Seamless Indicia Insertion
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Solution Overview
Problem
In multi-camera video systems, inserting realistic indicia downstream of the production switcher introduces delays when switching between camera views, as operators must manually identify the camera source, which can disrupt the seamless illusion of the indicia being part of the scene, especially in programs with rapid scene cuts.
Innovation Solution
An LVIS system with a front-end computing component that automatically identifies the camera source by processing video frames using a feature database and camera model estimation, allowing for rapid and accurate insertion of indicia without manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual camera identification is used downstream of the production switcher, then system complexity is reduced, but insertion delay increases and realism is compromised
Solution Approach 1:
The LVIS system automatically identifies the current camera source by analyzing video frame features independently, without requiring manual operator intervention. The system extracts features from video frames, compares them against a database of camera-specific features, and autonomously determines which camera is currently being displayed, enabling seamless indicia insertion across camera cuts
Solution Approach 2:
Camera-specific features are extracted and stored in a database before the broadcast event. During live operation, the system performs rapid feature matching against this pre-prepared database, eliminating the need for real-time manual camera identification and reducing insertion delay
2Extent of automation
If manual camera identification is used, then automation extent is reduced, but processing speed is sufficient for slow scene cuts
Solution Approach 1:
The manual mechanical process of operator-based camera identification is replaced with an automated computer vision system that extracts visual features from video frames and uses algorithmic comparison to identify the current camera source, dramatically increasing indicia insertion speed
3Productivity
If each camera has its own LVIS upstream, then indicia insertion speed is maximized, but system complexity and cost increase
Solution Approach 1:
A single downstream LVIS system is designed to handle multiple camera sources universally. The system maintains a database of features from all cameras and can identify and process indicia insertion for any camera source, replacing the need for separate LVIS units at each camera while maintaining insertion speed
Solution Approach 2:
Multiple separate LVIS systems, one for each camera, are merged into a single downstream LVIS system that processes all camera feeds centrally. The system combines the functionality of multiple units by implementing automatic camera source identification and unified indicia insertion control
Data Source
AI summary
An apparatus and method for receiving a current video frame of a program video having a plurality of scene cuts switching between video segments originating from respective cameras of a plurality of cameras and automatically identifying the camera from which the current video frame originated. The apparatus having a feature database storing predetermined features for a plurality of cameras filming a program video originating from the plurality of cameras, a recognition unit receiving a current video frame of the program video, extracting features from the current video frame, and comparing the features to the previously stored features and a camera model estimator computing an estimated camera model based on corresponding feature pairs and previously stored features, wherein if the estimated camera model is within a predetermined threshold of a stored camera model for one of the plurality of cameras, that camera is identified as originating the current video frame.