3D Object Modeling in Video Streams via Selective Segmentation
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Solution Overview
Problem
Current technologies face challenges in generating complete three-dimensional models from video recordings, resulting in unmanageable data and excessive computing power, as they fail to effectively capture and store depth information and object features, especially when objects move out of the frame or become obscured.
Innovation Solution
A system that identifies objects of interest, models them in three dimensions, and integrates depth information into the video stream, allowing for selective generation of three-dimensional data without requiring all video to be converted, using similar processing steps for various inputs, including topographical information, and projecting these models onto a sphere for interactive 360-degree environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complete three-dimensional models are generated from all video recordings, then object identification and depth information capture are improved, but data volume and computing power requirements become unmanageable
Solution Approach 1:
The system segments the video content by identifying and isolating only the objects of interest rather than processing the entire video stream. Users select specific objects to model, and the system generates three-dimensional representations only for those selected objects, reducing the overall data volume while maintaining measurement precision for the targeted objects.
Solution Approach 2:
The system extracts only the essential three-dimensional information needed for objects of interest from the video stream, rather than converting the entire video to three-dimensional data. This extraction approach retrieves depth information and spatial data only for the selected objects, significantly reducing data quantity while preserving measurement accuracy for those objects.
2Measurement precision
If complete three-dimensional models are generated from all video recordings, then object identification and depth information capture are improved, but computing power requirements become excessive
Solution Approach 1:
The computing process is segmented by processing only the portions of the video stream that contain objects of interest. The system divides the video into relevant segments corresponding to selected objects and applies three-dimensional modeling computations only to those segments, reducing overall computing power requirements while maintaining measurement precision for the targeted objects.
Solution Approach 2:
Instead of applying complete three-dimensional modeling to the entire video stream (excessive action), the system applies the modeling process only partially to the selected objects of interest. This partial action approach reduces computing power consumption while achieving the desired measurement precision for the objects that matter most to the user.
3Reliability
If all video content is converted to three-dimensional data, then object movement detection is improved, but processing time and computational resources increase
Solution Approach 1:
The video stream is segmented into portions containing objects of interest, and three-dimensional processing is applied only to those segments. This segmentation reduces processing time by avoiding unnecessary computation on background elements and non-selected objects, while maintaining reliable object movement detection for the selected objects through comprehensive three-dimensional analysis.
Solution Approach 2:
The system extracts and processes only the movement information for objects of interest rather than analyzing the entire video content. This extraction approach reduces processing time by focusing computational resources only on the objects that require movement detection, while maintaining detection reliability through thorough analysis of the extracted object data.
4Power
If selective object modeling is implemented, then computational resources are reduced, but the system complexity increases due to object identification and selection mechanisms
Solution Approach 1:
The system employs a multi-functional object identification and selection mechanism that can operate in multiple modes: manual object selection by users, automated object detection based on pre-defined criteria, and hybrid approaches. This universality allows the system to reduce computing power consumption through selective modeling while managing complexity by providing flexible selection mechanisms that adapt to different user needs and application scenarios.
Data Source
AI summary
A system for detecting and incorporating three-dimensional objects into a video stream reads an input video data stream. The user specifies areas of attention wherein said areas of attention or hotspots. Tracking movement of the hotspots generating a trajectory of said at least one object. Generating a cloud of points and tracking said points to detect configurations of points most similar to the initially defined hotspot. Obtaining a three dimensional topology defining a volume of interest in a three-dimensional space. Building virtual structures or pseudo objects that are placed within a spherical environment generated on the input video.