Once the machine learning model was trained, a web application was developed that allowed users to upload video footage from their surveillance cameras. The machine learning model was a convolutional neural network (CNN) that was trained to detect the presence of fire in an image. The project involved several steps, including collecting and labeling a dataset of video footage that contained both fire and non-fire events, preprocessing the video footage to extract individual frames, and training a machine learning model using the preprocessed dataset. The project aims to improve fire safety by detecting potential fire hazards early and allowing users to take appropriate action. The application uses computer vision algorithms and machine learning techniques to analyze video footage from the cameras and detect the presence of fire. The objective of this project is to develop a web application that uses surveillance cameras to detect fire and alert users in real-time.
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