Multi-Classification Detection on Live Video (Live Vision)

The proposed system, “Multi-Classification Detection on Live Video” is an intelligent computer vision–based platform designed to detect and classify multiple object categories in real-time video streams. The system processes live video input from cameras or video files and applies deep learning models to accurately identify and label objects across multiple predefined classes simultaneously. It supports real-time monitoring, automated detection, and visual annotation, enabling effective analysis of dynamic environments. The platform utilizes state-of-the-art convolutional neural network architectures such as YOLO, transfer learning–based models to achieve high-speed and accurate multi-class detection. Object classification and localization are performed frame by frame, ensuring consistent detection even under varying lighting and motion conditions. The system achieves an overall detection accuracy of up to 85-90%, with optimized inference speed suitable for real-time applications. A robust video processing pipeline handles frame extraction, pre-processing, object tracking, and result visualization. Detected objects are displayed with bounding boxes, class labels, and confidence scores. The system also supports recording and saving processed videos for further analysis. Performance evaluation is conducted using metrics such as precision, recall, F1-score, and FPS (frames per second). The application is developed using Python, OpenCV, and deep learning frameworks such as Yolov8, with a backend powered by Django for live streaming and control. The system is scalable and can be extended for applications including surveillance, traffic monitoring, smart cities, and security systems. Overall, the project demonstrates the effective use of deep learning and real-time video analytics for accurate multi-class object detection.