Video Analytics Sampling Model for Real-Time Detection
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Solution Overview
Problem
Current image and video analytics systems face challenges in providing reliable, accurate, and real-time detection of specific situations, patterns, movements, and behaviors, especially when processing large quantities of data from diverse scenes, viewpoints, and perspectives.
Innovation Solution
A computer-implemented method for sampling and analyzing data from image frames using a 3D-vector space-based sampling model, which defines areas of interest and extracts relevant data for analysis, allowing for efficient detection of predefined problems across different viewing perspectives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional image and video analytics systems process large quantities of data from multiple cameras and perspectives, then detection coverage and reliability are improved, but computational complexity and processing time increase significantly
Solution Approach 1:
The patent divides the image processing task into multiple segments by processing different regions of interest (ROIs) separately. Each ROI is defined by specific spatial coordinates and processed independently through the sampling model, allowing the system to handle large quantities of data from multiple cameras without processing the entire image at once, thus reducing computational complexity while maintaining detection reliability.
Solution Approach 2:
The patent extracts only the necessary data from specific regions of interest using a sampling model that selects representative samples from each ROI. This extraction approach allows the system to process large quantities of image data from multiple perspectives by focusing computational resources only on the most relevant areas, thereby reducing overall computational complexity while maintaining detection accuracy.
2Measurement precision
If traditional systems analyze all image data from multiple perspectives, then detection accuracy is improved, but processing speed and real-time capability deteriorate
Solution Approach 1:
The patent applies different sampling strategies to different regions of interest based on their local characteristics and importance. Each ROI is processed with appropriate sampling density and methodology tailored to its specific requirements, allowing the system to maintain high detection accuracy for critical areas while processing less important regions more quickly, thus improving overall processing speed without sacrificing accuracy.
Solution Approach 2:
The patent processes only the necessary portions of image data from multiple perspectives rather than analyzing all data completely. By using sampling models to select representative samples from each ROI, the system achieves sufficient detection accuracy without the computational burden of processing every pixel and data point, thereby improving processing speed and real-time capability.
3Adaptability or versatility
If traditional systems process data from diverse scenes and viewpoints, then detection versatility is improved, but system complexity and data management burden increase
Solution Approach 1:
The patent employs a universal sampling model that can be applied across multiple cameras and perspectives to extract consistent features and patterns. This multi-functional sampling approach allows the system to handle diverse scenes and viewpoints through a single unified methodology, improving detection versatility while avoiding the need for separate processing systems for each camera angle, thus reducing overall system complexity.
Data Source
AI summary
A computer-implemented method for sampling and analyzing data from at least one image frame from at least one series of image frames captured by at least one sensor, comprises: defining at least one sampling model, wherein the sampling model is defined in a virtual 3D-vector space and is based on one or more predetermined shapes in the virtual 3D-vector space, applying the at least one sampling model to at least one part of the at least one image frame of the at least one series of image frames, wherein applying of the at least one sampling model defines at least one area of the at least one image frame from which data is to be extracted, extracting data from the at least one area of the at least one image frame defined by the sampling model, and analyzing the extracted data.