Virtual Construct Grid for AI Object Detection
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Solution Overview
Problem
Current AI-based computer vision systems operating in 3D space face challenges in efficiently recognizing and tracking objects within a confined area, leading to increased power consumption, reduced accuracy, and higher CPU and bandwidth requirements due to the need to process all objects in a broader space, rather than focusing on objects within a defined region.
Innovation Solution
A system comprising sensor panels forming a 2D/2.5D/3D virtual barrier, coupled with a central processing module, which detects and identifies objects breaching a predefined grid or volume by using synchronized sensors and cameras to capture sharp images with low exposure times and high frame rates, thereby improving motion detection accuracy and reducing unnecessary processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If AI algorithms process all objects in a broader 3D space, then comprehensive object recognition is achieved, but power consumption increases and processing efficiency decreases
Solution Approach 1:
The patent divides the 3D space into multiple 2D/2.5D/3D virtual constructs (grids) that can be independently processed. Instead of analyzing all objects in the entire space simultaneously, the system segments the monitoring area into discrete virtual barriers, each processed by AI algorithms independently. This segmentation reduces the computational burden and power consumption while maintaining comprehensive object recognition across the entire space.
Solution Approach 2:
The patent applies different processing qualities to different spatial regions by creating virtual constructs with varying dimensions (2D, 2.5D, or 3D) based on local requirements. Areas requiring higher monitoring precision use 3D virtual constructs, while less critical areas use 2D constructs, optimizing power consumption according to local quality requirements rather than uniformly processing the entire space.
2Reliability
If AI algorithms process objects in a larger 3D space, then complete object tracking is achieved, but CPU requirements and bandwidth requirements increase
Solution Approach 1:
The system segments the large 3D monitoring space into multiple smaller virtual constructs, allowing CPU processing to be distributed across different grid sections. Each processor handles only the objects within its assigned virtual construct, reducing individual CPU requirements and bandwidth usage while maintaining complete object tracking through coordinated processing of all segments.
Solution Approach 2:
The patent transforms the traditional 3D space processing into a combination of 2D and 2.5D virtual constructs, reducing the computational complexity from full 3D analysis. By using 2D panels that detect motion in 3D space (2.5D), the system achieves complete object tracking with lower CPU and bandwidth requirements compared to processing entire 3D volumes.
3Measurement precision
If synchronized sensors with high frame rates are used, then motion detection accuracy improves, but device complexity and power consumption increase
Solution Approach 1:
The patent merges multiple synchronized sensors into a unified virtual construct processing system. Instead of independently managing complex synchronization across separate sensor systems, the sensors are integrated into a coordinated array that processes motion detection within defined virtual barriers. This merging approach maintains high frame rates and motion detection accuracy while reducing overall system complexity through unified processing.
Data Source
AI summary
Al-based computer vision algorithms, operating in the real world rather than in the digital domain, typically operate in a certain three-dimensional space. The system, programs and method provided herein, describe a system that allows limiting the execution of Al algorithms to operate only on objects breaching a predefined and confined plane (also termed grid) or a volume in space. In other words, the system programs and method provided herein define a 2D/2.5D/3D regions or grid in space, operable to detect any change which occurs in and through this grid. This ability includes in certain implementations, the detection of any animate or inanimate object, or multiple grouped objects which may cross, pass or introduced to this grid, their type, identification and action assigning.