Object Detection and Annotation in Videos Using Deep Learning

Abstract

This project develops an advanced video processing system capable of detecting and annotating objects in video footage. By leveraging deep learning-based object detection models, the system identifies, tracks, and labels objects across video frames, ensuring accurate analysis over time. The implementation uses Flask as a web framework, OpenCV for video handling, and the YOLO model for efficient object detection. Users can upload videos, specify target objects, and receive annotated outputs with bounding boxes and confidence levels. The system supports real-time processing, enabling automated monitoring applications such as surveillance, traffic analysis, and security enforcement. A Firebase backend facilitates cloud storage and retrieval of processed videos and detection results. Optimized frame sampling ensures efficient computation, reducing processing time without compromising accuracy. The application enhances decision-making by providing structured data insights extracted from video content. Additional features include detection result saving, cloud synchronization, and historical data access for further analysis. The system's flexible architecture allows future enhancements, including integration with live camera feeds and expanded detection capabilities. Key Words:Video Processing, Object Detection, Annotation, Computer Vision, Automated Monitoring, Surveillance, Deep Learning.