Temporal Graph Partitioning for Automated Video Object Extraction
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Solution Overview
Problem
Manual extraction of objects from video streams for interactive video hyperlinks is time-consuming and not scalable with increasing video content on the Internet, as users cannot currently interact with video content in a meaningful way.
Innovation Solution
A computer-implemented method for automated object creation by partitioning a temporal graph, which involves segmenting images, computing motion vectors, establishing segment correspondence, constructing a temporal graph, and partitioning it to identify coherently moving objects, allowing for efficient extraction and separation of objects from the background and other objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual outlining is used to determine object location and identity in video streams, then object extraction accuracy is improved, but time consumption and cost increase significantly
Solution Approach 1:
The patent replaces the manual mechanical outlining process with an automated computer-implemented system. The system uses motion vector computation and temporal graph partitioning algorithms to automatically identify and extract objects from video streams, eliminating the need for manual frame-by-frame outlining while maintaining object extraction accuracy.
Solution Approach 2:
The system enables self-service object extraction by automatically analyzing video frames, computing motion vectors, constructing temporal graphs, and partitioning graphs to identify objects without human intervention. The algorithm autonomously determines object boundaries and tracks objects across frames, making the process scalable and efficient.
2Measurement precision
If manual outlining is used for object extraction, then object identity determination is improved, but scalability deteriorates with increasing video content
Solution Approach 1:
The patent replaces manual object identification processes with automated computer vision algorithms. The system computes motion vectors to track object movement and uses temporal graph partitioning to identify coherent objects across multiple frames, enabling automatic object identity determination that scales with increasing video content volume.
Solution Approach 2:
The system creates and processes digital representations of video data through temporal graphs, where nodes represent image segments and edges represent temporal relationships. This digital modeling approach enables efficient automated analysis and object identification without manual intervention, significantly improving scalability.
3Productivity
If automated object extraction is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent segments the complex task of object extraction into distinct computational stages: motion vector computation, temporal graph construction, and graph partitioning. Each stage processes specific aspects of the video data independently, making the overall system more manageable and implementable despite the complexity of the automated approach.
Solution Approach 2:
The patent introduces temporal graphs as an intermediary data structure between video input and object output. The graphs serve as a mediator that organizes image segments and their temporal relationships, simplifying the computation and enabling efficient automated object extraction while managing system complexity.
Data Source
AI summary
One embodiment relates to a computer-implemented method for the automated extraction of objects from a video stream. The method includes an automated procedure for creating a temporal graph, and an automated procedure for cutting the graph into graph partitions. The method further includes an automated procedure for mapping the graph partitions to pixels in frames of the video stream. Other features, aspects and embodiments are also disclosed.