Deepankar Sharma

Smart Surveillance System using Computer Vision

This is the part of the final year group project in BCA. The aim of this project is to develop a smart surveillance system that can be integrated with existing surveillance systems to provide advanced features like motion detection using contours and real-time people tracking using YOLOv3. The system uses computer vision algorithms to analyze the video feed from surveillance cameras and identify areas of motion, while object detection algorithms like YOLOv3 are used to detect and track people in real-time.

The project requires expertise in computer vision, machine learning, and software development. Large datasets of labeled video footage are needed to train and test the object detection and motion tracking algorithms. Powerful hardware is also required to process the video feed in real-time.

The smart surveillance system has several potential benefits, such as reducing false alarms and improving the accuracy of surveillance systems. It can also enable security personnel to monitor and track people of interest more effectively.

Twilio is used in the Smart Surveillance System for sending alerts to the users via Whatsapp in case of any suspicious activity.

In conclusion, the Smart Surveillance System is developed using a combination of various software tools and technologies, including Python, OpenCV, YOLOv3, Streamlit, TensorFlow, NumPy, Twilio, SQLite, and other libraries and technologies. These tools and technologies provide the necessary functionality and features required for the development of an advanced surveillance system.