Industrial Floor Video Analytics for Safety and Productivity Tracking
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Solution Overview
Problem
Existing systems for monitoring safety and productivity on industrial shop floors require significant manual intervention, leading to biased and less accurate results, and there is a need for improved systems to prevent worker injuries and track productivity metrics.
Innovation Solution
A method and system using advanced camera analytics to automatically detect safety non-compliance and monitor performance by processing image and video data, applying artificial intelligence for tasks such as object tracking, pose estimation, and key-point estimation, and generating alerts and instructions to correct non-compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual intervention is used to monitor safety and productivity, then human judgment and flexibility are applied, but the results are biased and less accurate
Solution Approach 1:
The patent replaces manual monitoring (mechanical human observation and assessment) with an automated computer vision system using cameras, machine learning models, and image processing algorithms. This substitution eliminates human bias and improves measurement accuracy while maintaining system functionality through automated detection of safety violations and productivity metrics.
Solution Approach 2:
The system enables self-monitoring of safety and productivity by automatically capturing images, processing them through machine learning models, and generating compliance reports without requiring manual intervention. The automated system serves itself by performing data collection, analysis, and reporting functions that would otherwise require human operators.
2Productivity
If manual monitoring methods are used, then system simplicity is maintained, but significant manual intervention is required which reduces productivity
Solution Approach 1:
The patent implements continuous automated monitoring through cameras that continuously capture images and video feeds, with machine learning models continuously processing this data to detect safety violations and track productivity metrics. This continuous automated operation eliminates interruptions and maintains constant surveillance, significantly improving monitoring productivity compared to periodic manual checks.
Solution Approach 2:
The system replaces time-consuming manual monitoring activities with automated computer vision processing, where images are automatically analyzed by machine learning models to identify safety violations and productivity indicators, eliminating the time loss associated with manual intervention while maintaining comprehensive monitoring coverage.
3Measurement precision
If automated camera analytics are implemented, then measurement precision and productivity are improved, but the device complexity increases
Solution Approach 1:
The patent segments the complex monitoring task into distinct functional modules: image capture by cameras, pre-processing of image data, machine learning model processing for specific detection tasks (PPE detection, lifting operation detection, vehicle proximity detection), and post-processing for report generation. This segmentation manages system complexity by organizing functions into separate, manageable components that can be independently optimized and maintained.
Solution Approach 2:
The patent creates a universal monitoring system that uses the same camera infrastructure and machine learning platform to perform multiple detection functions simultaneously - personal protective equipment compliance, lifting operation safety, vehicle proximity monitoring, and productivity tracking. This multi-functionality reduces overall system complexity compared to implementing separate specialized systems for each monitoring task.
Data Source
AI summary
Safety non-compliance and, simultaneously, productivity metrices are computed and tracked. Objects of interest (such as technicians, tools, cranes, slings, gloves, assets) are identified and tracked from real time streams of cameras through the application of several filtration processes (boundary based, aspect-ratio based, intersection-over-union based, dice coefficient-based, and custom model bounding box filtration techniques) and are then projected onto a 2D homographic map which localizes their position. The localized position is filtered using custom time series and frequency-based filters and aggregated over a set of defined time ranges to obtain productivity and safety KPIs. Non-compliance detections are flagged with video recorded for the given time duration and anonymized with custom processes to ensure data privacy. Productivity KPIs are represented in a dashboard with readings collected at different time intervals like hourly, daily, weekly and monthly.