TeleICU Patients Video Processing and Deep Learning This project is a real-time video processing and deep learning system designed for monitoring ICU patients. The system captures live video feed from CCTV cameras, extracts frames every 10 seconds, preprocesses the frames, and analyzes them using a deep learning model to detect any anomalies in the patient's condition.

Requirements Python 3.x OpenCV library (install with pip install opencv-python) schedule library (install with pip install schedule) Setup Replace video_link with the path to your video file or the URL of the CCTV camera. Run the script using Python (e.g., python video_processing.py). The script will start extracting frames from the video and saving them to the frames folder. Components Video Capture and Frame Extraction The system uses OpenCV to capture live video feed from CCTV cameras. It extracts frames every 10 seconds using a scheduling library like schedule in Python. The extracted frames are preprocessed to resize, normalize, and convert them into a suitable format for analysis.

Feature Extraction and Deep Learning Model The system uses a deep learning model to analyze the preprocessed frames and detect anomalies. The model is designed using Convolutional Neural Networks (CNNs) for image classification or object detection. The model is trained to detect anomalies in the patient's condition.

Anomaly Detection and Emergency Signal Generation The system uses an anomaly detection algorithm to identify unusual patterns or behaviors in the extracted features. Techniques like One-Class SVM, Local Outlier Factor (LOF), or Isolation Forest are used to detect anomalies. If an anomaly is detected, the system generates an emergency signal that can be sent to healthcare professionals or trigger an alert system.

Acknowledgments OpenCV: https://opencv.org/ schedule: https://schedule.readthedocs.io/en/stable/ One-Class SVM: https://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html Local Outlier Factor (LOF): https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.LocalOutlierFactor.html Isolation Forest: https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.IsolationForest.html