Detection and Identification of Objects and Humans in Thermal Images
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Intelligent detecting processing capabilities can be instrumental to improving safety and efficiency for firefighters and victims during firefighting activities. The objective of this research, is to create an automated system that is capable of real-time object detection and recognition that improves the situational awareness of firefighters on the scene. We have explored state of the art Machine Learning (ML) techniques to achieve this objective. The goal for this work is to maximize the situational awareness for fire-fighters by effectively exploiting the information gathered from the infrared camera and use a trained deep Convolutional Neural Network (CNN) system to classify and identify objects of interest in real time. In the midst of such critical circumstances created by a fire, this system is able to accurately inform the decision making process of firefighters in real time by extracting crucial information for processing. It is then able to make inferences about these circumstances to aid firefighters in safely navigating such hazardous and catastrophic environments.
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