Volumetric Modeling for Camera Image Selection in Pattern Recognition
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Solution Overview
Problem
In pattern recognition systems, identifying the portion of an image that contains an object or person for accurate recognition is challenging, especially when multiple cameras capture overlapping views of the same area, leading to errors in object or person identification.
Innovation Solution
A computer-implemented method using volumetric modeling to determine which camera images contain relevant features for pattern recognition, involving a system of cameras and a volumetric recognition engine that generates 3D projections and identifies moving objects within a building, then provides specific image regions to alternative recognition systems for accurate feature extraction and identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple cameras capture overlapping views of the same area, then the coverage and detection capability are improved, but the accuracy of object or person identification deteriorates due to errors in recognition
Solution Approach 1:
The patent segments the 3D space into discrete volumetric cells using a grid structure. Each camera's field of view is projected onto this 3D grid, and objects are identified by analyzing patterns of occupied cells across multiple cameras. This segmentation allows the system to process overlapping camera views systematically by dividing the continuous space into manageable discrete units, resolving the contradiction between multi-camera coverage and identification accuracy.
Solution Approach 2:
The patent transitions from 2D image plane analysis to 3D volumetric analysis by projecting camera images onto a three-dimensional grid space. This dimensional transformation enables the system to disambiguate objects captured in overlapping 2D views by analyzing their spatial relationships in 3D, thereby maintaining identification accuracy while utilizing multiple camera perspectives for enhanced coverage.
2Measurement precision
If volumetric modeling is used to identify relevant image areas, then the precision of pattern recognition is improved, but the complexity of the system increases
Solution Approach 1:
The patent uses a simple 3D grid structure composed of discrete volumetric cells as a temporary data representation. This grid is constructed and discarded for each analysis cycle, requiring minimal computational resources. The simplicity of the grid-based approach provides accurate volumetric modeling without introducing complex data structures, algorithms, or hardware components, thus achieving high precision while maintaining low system complexity.
3Productivity
If specific image regions are isolated for feature extraction, then the efficiency of pattern recognition is improved, but the time required for processing increases
Solution Approach 1:
The patent performs preliminary processing by projecting all camera images onto the 3D grid and identifying occupied volumetric cells before actual pattern recognition begins. This pre-processing step organizes the data spatially and identifies regions of interest in advance, allowing the subsequent pattern recognition to focus only on relevant areas. This preliminary organization reduces the computational burden during recognition, improving efficiency without significant time penalty.
Data Source
AI summary
A computer-implemented method performs volumetric modeling of objects in a space captured by a plurality of cameras to determine which of a plurality of camera images contains an image that facilitates pattern recognition. The camera images that contain an image that facilitates pattern recognition are then used to perform pattern recognition.