Project Title: “Zero-Shot Learning for Smart Surveillance and Emergency Response”

Objective: The aim of this project is to develop a smart surveillance system using existing CCTV infrastructure that can detect and classify emergencies and unusual activities, including those that the model has not seen during training.

Project Description:

Data Collection: Use the video feeds from the existing CCTV cameras installed around the town.

Model Training: Train a Zero-Shot Image Classification model using a dataset of various emergency situations like accidents, fires, or criminal activities. You can use the openai/clip-vit-large-patch14-336 model as a starting point and fine-tune it on your dataset.

Emergency Detection: Use the trained model to monitor the CCTV feeds in real-time and detect any emergencies. This could involve object detection (e.g., detecting vehicles or people), action recognition (e.g., detecting accidents or fights), or anomaly detection (e.g., detecting unusual activities).

Zero-Shot Classification: Use the trained model to classify images of emergency situations that were not included in the training data. This will demonstrate the zero-shot learning capabilities of the model.

Alert System: When an emergency is detected, the system should automatically alert the relevant authorities such as the police, fire department, or medical services. The alert could include details like the type of emergency, location (based on the CCTV camera), and time.

Evaluation: Evaluate the performance of the system in terms of its accuracy in detecting and classifying emergencies and the speed of alerting the authorities.

User Interface: Develop a user interface for the authorities to monitor the alerts and respond to them. This could be a web application that shows the real-time CCTV feeds, detected emergencies, and status of the alerts.