Multi-Camera Worker Re-Identification for PPE Compliance Monitoring
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing site safety monitoring systems in construction sites are labor-intensive and prone to errors, failing to continuously track worker movements and safety compliance across large areas due to limitations in image processing techniques, particularly in identifying personal protective equipment (PPE) usage and worker re-identification.
Innovation Solution
A system utilizing a computing platform with multiple cameras and deep learning models for re-identification and classification, employing a similarity loss function for robust worker tracking and a weighted-class strategy for PPE classification, to enhance safety compliance monitoring by identifying and logging incidents of non-compliant behavior.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual monitoring by safety officers is used, then safety compliance can be monitored, but the process is labor-intensive and error-prone
Solution Approach 1:
The patent replaces manual mechanical monitoring by safety officers with an automated computer vision system using deep learning models. The system uses CNNs for worker detection, re-identification, and PPE classification to automatically monitor safety compliance, eliminating human labor intensity and reducing errors associated with manual observation.
Solution Approach 2:
The system enables self-service monitoring where the computer vision system autonomously performs safety compliance checks without requiring continuous human intervention. The deep learning models automatically detect workers, track their movements across cameras, classify PPE usage, and identify non-compliant behaviors, allowing the system to monitor itself continuously.
2Measurement precision
If single camera imaging is used, then individual workers can be monitored, but continuous tracking across large sites is not possible
Solution Approach 1:
The patent merges multiple camera views into a unified monitoring system. The re-identification model processes images from multiple cameras and associates workers across different camera feeds, creating a comprehensive tracking system that covers large construction sites while maintaining precise worker identification and tracking continuity.
Solution Approach 2:
The system transitions from single-camera two-dimensional imaging to multi-camera three-dimensional spatial tracking. By incorporating camera position and viewing angle information, the system achieves accurate worker re-identification and continuous tracking across large areas, effectively adding a spatial dimension to the monitoring capability.
3Measurement precision
If deep learning models are trained with supervised learning, then accurate worker identification and PPE classification can be achieved, but training requires large amounts of manually labeled data
Solution Approach 1:
The patent performs preliminary actions by pre-training the deep learning models on large datasets of worker images and PPE examples before deploying them for specific construction site monitoring. This pre-training phase establishes the foundation for accurate re-identification and classification, reducing the need for extensive site-specific manual labeling and accelerating the overall training process.
Data Source
AI summary
A system for monitoring safety compliance comprises a plurality of cameras and a computing system. The plurality of cameras are configured to obtain data, and the data comprises multiple images associated with one or more objects. The computing system is configured to process the data to determine the safety compliance of the one or more objects associated with the multiple images based on implementing a first model trained for re-identification. The computing system is further configured to train the first model for re-identification, by determining a similarity loss and updating the first model based on the similarity loss.