Neural Network Worker Recognition for Multi-Camera Trade Tracking
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Solution Overview
Problem
Managing large industrial sites is challenging due to the difficulty in tracking workers and contractors, as traditional monitoring methods are inefficient and require extensive manual review of vast amounts of video data, and existing computerized systems struggle with heterogeneous devices and non-standardized data, leading to delayed results.
Innovation Solution
A neural network-based approach using convolutional neural networks (CNN) and recurrent neural networks (RNN) processes images from cameras to identify workers through color-coded stickers and sensor meshes, determining trade-specific activities and backgrounds to accurately count and classify workers by trade.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional manual monitoring methods are used to track workers on industrial sites, then accountability and productivity assessment can be achieved, but the process becomes tedious and time-consuming due to extensive manual review of vast amounts of video data
Solution Approach 1:
The patent replaces manual mechanical review of video data with an automated computerized system that uses neural networks and image processing algorithms to detect, track, and classify workers. The system automatically processes video feeds from multiple cameras, eliminating the need for human reviewers to manually examine vast amounts of footage while maintaining high accuracy in worker identification and trade classification.
2Area of stationary object
If computerized systems with multiple heterogeneous devices are deployed to monitor industrial sites, then comprehensive worker tracking is possible, but the system becomes complex and difficult to manage due to different frame rates, time delays, and resolutions
Solution Approach 1:
The patent implements a universal processing framework that handles multiple camera inputs with different specifications (frame rates, resolutions, time delays) through a standardized neural network architecture. The system normalizes and synchronizes data from heterogeneous devices, allowing a single multi-functional system to process diverse camera feeds without requiring separate processing pipelines for each device type.
Solution Approach 2:
The system dynamically adjusts processing parameters such as frame sampling rates, resolution scaling, and time synchronization offsets based on the specific characteristics of each camera device. By automatically modifying these parameters, the system harmonizes inputs from heterogeneous devices with different technical specifications, enabling seamless integration and processing of data from multiple sources.
3Measurement precision
If specialized personnel are trained to process collected video data, then accurate worker identification can be achieved, but extensive and continuous training is required due to the complexity and non-standardized nature of the data
Solution Approach 1:
The patent replaces the need for specialized human personnel with an automated neural network-based system that performs worker identification and trade classification. The system uses pre-trained convolutional neural networks (CNNs) that have been trained on diverse worker images, eliminating the need for ongoing training of human operators while maintaining high accuracy in identifying workers and their trades across varying conditions.
Data Source
AI summary
An example computing platform comprising is configured to (i) receive, via one or more cameras positioned on a construction site, a plurality of images, (ii) detect, within the plurality of images, a plurality of objects being worn by respective workers on the construction site, (iii) select, from the plurality of images, a set of images depicting a particular worker, and (iv) based on the selected set of images depicting the particular worker, determine a plurality of trade probabilities for the particular worker, each trade probability in the plurality of trade probabilities indicating a likelihood that the particular worker belongs to a particular trade from among a plurality of trades.