3D Object Tracking in Video for Interactive VR Scenes
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Solution Overview
Problem
Existing video technologies fail to effectively capture and maintain the three-dimensional nature of objects in video recordings, leading to loss of depth information and excessive computational requirements when attempting to create complete three-dimensional models.
Innovation Solution
A system that identifies objects of interest, tracks their movement, and integrates depth information into video streams, allowing for selective three-dimensional modeling without requiring excessive computing resources, using both video and topographical data to create interactive 360-degree environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the system attempts to create complete three-dimensional models of all objects in the video, then the three-dimensional representation quality is improved, but the computational power and data volume requirements become excessive
Solution Approach 1:
The system segments the video content by identifying and isolating specific objects of interest from the overall scene. Instead of processing all objects uniformly, the system selectively models only those objects that meet predefined criteria (such as user interaction potential, visual prominence, or semantic importance), thereby reducing computational complexity while maintaining high three-dimensional model quality for relevant objects
Solution Approach 2:
The system applies different processing quality levels to different regions and objects within the video. Objects designated as 'objects of interest' receive high-quality three-dimensional modeling with detailed geometry and texture, while background elements and non-critical objects are processed at lower detail levels or omitted entirely, optimizing the balance between model quality and computational resource consumption
2Loss of information
If the system converts entire video streams to three-dimensional data, then the completeness of three-dimensional information is improved, but the data volume and storage requirements become unmanageable
Solution Approach 1:
The system extracts only the essential three-dimensional information required for representing objects of interest, separating this critical data from the redundant information present in complete video stream conversion. By extracting solely the geometric, textural, and spatial data necessary for meaningful three-dimensional representations, the system minimizes data volume while preserving depth information for relevant objects
Solution Approach 2:
The system performs partial conversion of video data to three-dimensional representations, processing only the subset of frames and objects that are most important for maintaining depth information. This selective approach avoids the excessive data generation that would result from converting entire video streams, achieving sufficient three-dimensional representation without unnecessary data accumulation
3Reliability
If the system models all objects in the video as three-dimensional, then the completeness of object representation is improved, but the processing time and computational cost increase excessively
Solution Approach 1:
The system dynamically adjusts its processing strategy based on the video content and user needs. By continuously analyzing video frames to identify objects of interest, the system can adaptively allocate computational resources in real-time, focusing processing power on objects that require three-dimensional modeling while efficiently handling or skipping processing of objects that do not meet the criteria, thus maintaining representation completeness for relevant objects while improving overall processing efficiency
Data Source
AI summary
A method for detecting and incorporating three-dimensional objects into a video stream reads an input video data stream is described. A method may read an input video and may accept hotspots defining at least one real-world object of interest shown within the video. A method may track movement of any hotspots generating a trajectory of any objects of interest in two-dimensional space of the video and may obtain a three-dimensional topology defining a three-dimensional volume of interest in a three-dimensional space. The method may translate the hotspots to the three-dimensional volume and may calculate motion of the hotspots in the three-dimensional space. A method may build virtual structures to relate any hotspots to the three-dimensional topology to create a three-dimensional geometric shape and finally project the output, if needed.