Foreground Object Removal in Moving Camera Video
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Solution Overview
Problem
Existing methods for removing moving objects from video scenes often leave artifacts and are not suitable for studio settings, as they rely on manual processes or incomplete AI solutions.
Innovation Solution
The method involves creating a background data model by analyzing frames where the foreground object is not present, using statistical analysis and depth information to replace pixel values, and transforming frames to maintain consistent camera perspective, allowing for accurate removal of foreground objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual object removal is used, then removal accuracy is high, but processing time and labor cost increase significantly
Solution Approach 1:
The system performs automatic object removal without manual intervention by analyzing video frames and autonomously generating masks to identify and remove objects, replacing the manual process while maintaining accuracy
Solution Approach 2:
The patent replaces manual mechanical selection processes with automated computational algorithms that use image processing and machine learning to identify and remove objects automatically
2Loss of time
If existing AI-based removal methods are used, then processing time is reduced, but artifacts and poor quality results are generated
Solution Approach 1:
The system uses feedback mechanisms where the generated masks are refined through iterative processes, and the results are validated against the original video frames to ensure quality before final output
Solution Approach 2:
The patent performs preliminary analysis of multiple video frames to build comprehensive background models and object tracking data before executing the actual removal, ensuring high-quality results are prepared in advance
3Measurement precision
If background data model is built from all frames, then statistical accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the video sequence into different frames and processes them in batches, analyzing only the necessary subset of frames for building background models while maintaining statistical accuracy through selective sampling
Solution Approach 2:
The system uses partial action by analyzing a representative subset of frames rather than all frames, achieving sufficient statistical accuracy without the full computational burden of processing every frame
Data Source
AI summary
Methods, an apparatus, and software media are provided for removing unwanted information such as moving or temporary foreground objects from a video sequence. The method performs, for each pixel, a statistical analysis to create a background data model whose color values can be used to detect and remove the unwanted information. The method assumes that for each pixel the background is present in a majority of the frames. The camera that records the video sequence may move relative to the geometry of the video scene. A pixel in a first frame is matched to a location in the geometry. The method determines color values of pixels, matched to the location in the geometry, in successive frames and clusters color values to determine a background color value range. It may use quadratic or better interpolation and extrapolation to determine background color values for unavailable frames.