THERMAL NIGHT VISION IMAGE AND VIDEO PROCESSING ANALYSIE OBJECT IN FOREST SAFETY

Abstract

This study presents a comprehensive model for processing thermal night vision images and videos, focusing on object detection, recognition, and analysis in low-light or no-light environments. The proposed system leverages advanced image processing techniques combined with machine learning algorithms to enhance visibility, reduce noise, and accurately identify objects based on their thermal signatures. The model incorporates real-time video processing capabilities to support dynamic and static object tracking with precision. Its applications span diverse domains, including surveillance, wildlife monitoring, and search and-rescue operations. Experimental results demonstrate the model's efficiency in improving object detection accuracy, even under challenging environmental conditions. When time goes by, human beings are advancing in technology, artificial and natural disasters are drastically increasing. The forest fire is one of the hazards. Forest fire incinerates trees that provide us with oxygen and if it is not detected early, it is very elusive to stop a forest fire from continue burns. The project's objective is to capture infrared image of forest fire detection using the appropriate camera, detect fire with RGB and YCbCr colour model to isolate fire pixels from the background and separate luminance and chrominance from the original image, and filter image using MATLAB Analyzer to process images.

Author

DINESH V, SIVAMOORTHY M, VIGNESHWARAN T, Ms.AARTHY R