Case Study: Thermal Image Recognition – Smart Buildings

 

Introduction

 
Our client from the commercial real estate industry wanted to understand the occupancy rate of their building and how to optimize it for energy efficiency. The goal was to create a tool that could use thermal images captured by cameras located in the area to determine the number of people present and identify their activities (sitting, standing, and moving).
 

Our Achievements

 
  • Developed a thermal image recognition system analyzing over 52,000 images
  • Improved the accuracy of detecting occupancy and activities within smart buildings
  • Received positive feedback for the system’s effectiveness in optimizing building management
  • Enhanced space usage monitoring with minimal privacy invasion
 

Client Benefits

 
  • Provided metrics on occupancy rates and activity patterns
  • Enhanced energy efficiency through data-driven insights
  • Supported effective space management with minimal disruption to occupants
 

The Problem

 
The client faced several challenges in their smart building management initiatives, including:

  • Complexities in analyzing large volumes of thermal data
  • Delays in data processing with initial software setups
  • Absence of a unified system for effective data analysis
  • Inconsistencies in sensor performance, affecting data accuracy
 

Our Solution

 
The initiative in smart building management was strategically designed to utilize thermal image recognition technology to detect occupancy and activities within a building accurately. The goal was to make predictions about room usage more precise and actionable through an AI-driven platform that analyzed data from IR sensors in real-time. Driven by the need to enhance building efficiency and streamline data analysis, our solution focused on developing a robust system using a Lepton FLIR sensor and Raspberry Pi. This system transformed complex thermal data into clear, predictive insights. This approach not only facilitated better understanding and quicker adaptations by building managers but also supported a proactive strategy in space utilization. The successful implementation of this model marked a significant advancement in smart building analytics, providing a powerful tool for optimizing energy usage and improving management practices.  

Path to Success

 
  • Defining the Problem and Objectives: The project started with a clear understanding of the need to optimize building occupancy rates and energy efficiency. The primary goal was to develop a tool that could detect the presence and activities of people in a building using thermal imaging.

  • Equipment Selection and System Development: Selecting the right equipment, such as the Lepton FLIR sensor and Raspberry Pi, was crucial. These components needed to work together seamlessly to gather and process data effectively.

  • Prototype Creation and Data Gathering: A prototype was created using the selected equipment. This setup served as a platform for initial testing and data collection, critical for refining the technology and preparing for broader application.

  • Data Analysis and Algorithm Training: Specific images from the data collected were manually processed to train the AI algorithm. This step involved marking the number of people and their activities within the images, which was essential for teaching the system how to recognize these patterns autonomously.

  • Implementation and Results Evaluation: The final tool was implemented to analyze the movement and occupancy within spaces, providing insights on space utilization with minimal privacy invasion. This phase was crucial for validating the effectiveness of the solution and making necessary adjustments based on real-world performance.
 

Results and Benefits

 
The Pragmile High Performance Team developed and refined a thermal image recognition tool that accurately analyzes occupancy and activities in a room, processing over 52,000 images. It was able to accurately identify postures like sitting, standing, among others. This tool enabled the client to monitor and optimize space usage efficiently, providing insights into frequently used areas and locations less frequented by occupants.  

Conclusion

 
The project successfully resulted in the development of a tool that could analyze thermal images to detect the presence and activities of people in a room. The challenges faced during the project were overcome by using a prototype and a purpose-built dataset to maximize the artificial intelligence learning process. The final tool was able to provide valuable insights while respecting privacy. Key takeaways from the client’s journey:

  • Developing a precise thermal imaging system is pivotal for enhancing space utilization and improving energy management in buildings.

  • Choosing the right hardware and software is fundamental to handling and analyzing the complex data derived from IR sensors.

  • Iterative testing and data validation are crucial to refine the AI model that accurately predicts room occupancy and activity patterns.

  • Real-time data processing and proactive system adjustments are necessary to maintain the accuracy and reliability of the thermal image recognition.
 

What Pragmile Offers

 
As Pragmile continues to push the boundaries of innovation, businesses that partner with us can expect to benefit from their deep expertise, cutting-edge technologies, and commitment to delivering bespoke solutions. By harnessing the power of AI, Computer Vision, and Machine Learning, Pragmile empowers organizations to build a future where ideas meet expertise, driving transformative results across industries.

Contact us directly at contact@pragmile.com and let’s discuss how innovative technologies and processes can transform your business.

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