Computer Vision Video Metrics for Automated Motion Analysis
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Solution Overview
Problem
Conventional video analysis systems require intensive human review and are prone to errors due to fatigue and distractions, and existing computer vision tools lack robust metric reporting based on motion analysis within video feeds.
Innovation Solution
A method and system that utilize a combination of non-learning and learning-based algorithms to identify and track objects of interest in video feeds, generating contextual metrics using computational geometry and neighborhood-based tracking, and reporting policies for automated video analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual human analysis of recorded activities is used, then detailed review can be performed, but it is time consuming, laborious, and prone to human error
Solution Approach 1:
The patent replaces manual human analysis with an automated computer vision system that processes video feeds to identify objects, track their movement, and generate metrics. This substitution eliminates human fatigue and distractions while providing consistent, scalable analysis without requiring intensive human review time.
Solution Approach 2:
The system enables self-service automated analysis where the computer vision algorithms independently process video data, identify objects of interest, track their motion, and generate contextual metrics without requiring human intervention for each analysis task, thereby eliminating time-consuming manual review while maintaining accuracy.
2Loss of information
If existing computer vision tools are used for object identification, then basic object presence can be detected, but robust metric reporting based on motion analysis is not provided
Solution Approach 1:
The patent merges object identification capabilities with motion tracking and contextual metric generation into a unified system. By combining computer vision-based object detection with neighborhood-based tracking algorithms and computational geometry methods, the system provides both accurate object identification and robust metric reporting on motion activities within areas of interest.
Solution Approach 2:
The system achieves multi-functionality by integrating multiple capabilities: object identification, motion tracking, contextual metric determination, and automated report generation. This universal system handles diverse monitoring needs including safety compliance, equipment monitoring, and activity analysis within a single platform, greatly enhancing adaptability and versatility.
3Reliability
If automated video analysis is implemented, then human error is reduced, but system complexity increases
Solution Approach 1:
The patent segments the automated video analysis system into distinct functional modules: video feed processing, object identification, motion tracking, metric determination, and report generation. This segmentation allows each component to be optimized independently while maintaining overall system reliability and reducing errors through specialized processing at each stage.
Data Source
AI summary
The invention relates generally to a method and system that provides robust metric reporting based on analysis of computer vision derived-video data. The invention utilizes learning-based methods for object identification, object localization, and contextual analysis in order to generate insights into, for example, efficiency, productivity, design and planning, and health and safety compliance in a workflow environment.