Multi-Classification Detection on Live Video (Live Vision)

Abstract

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.

Introduction

The text presents a study on real-time multi-class object detection in live video streams, a key area of computer vision that involves identifying objects and their locations in images or video frames. Live video detection adds challenges like motion blur, dynamic backgrounds, lighting variations, and strict latency requirements. Recent advances in deep learning, particularly Convolutional Neural Networks (CNNs) and single-stage detectors like YOLOv8, have enabled fast and accurate detection suitable for real-time applications.

Problem: Traditional object detection methods suffer from low accuracy, high computational costs, inability to handle multiple classes efficiently, and reliance on human supervision, limiting their use in live video systems.

Objectives: The proposed system aims to develop a robust, scalable, and real-time object detection framework capable of detecting multiple objects simultaneously from live video streams. It prioritizes high accuracy, minimal latency, and modularity for future extensions.

Literature Review:

• Traditional methods: Haar Cascades, HOG, and SVMs had limited accuracy and poor multi-object handling.

• Deep learning methods: R-CNN variants improved accuracy but were computationally intensive.

• Single-stage models: SSD and YOLO offered faster, real-time detection. YOLOv8 enhances multi-class detection, speed, and accuracy.

Proposed Methodology:

• Captures live video frames.

• Preprocesses frames (resize, normalize, noise reduction).

• Detects and classifies objects using YOLOv8.

• Post-processes results (bounding box refinement, duplicate removal, confidence scoring).

• Visualizes results with labeled bounding boxes.

System Architecture:

1. Input Module: Captures live video.

2. Preprocessing Module: Prepares frames for the model.

3. Detection & Classification Module: Detects and classifies objects using deep learning.

4. Output Module: Displays results with bounding boxes and labels.

Dataset: Annotated images with multiple object classes, split into training and testing sets.

Implementation: Python backend, Django framework, OpenCV for computer vision, YOLOv8 model, and frontend with HTML/CSS/JS.

Results:

• Accuracy: 85%

• Precision: 90.92%

• Recall: 83.33%

• F1-Score: 86.96%
  The system achieves high accuracy and real-time performance.

Applications: Surveillance, traffic monitoring, smart cities, industrial automation, public safety.

Advantages: Real-time detection, high accuracy, multi-class support, scalable architecture.

Limitations: Hardware-dependent performance, reduced accuracy in poor lighting, requires large labeled datasets.

Future Scope: Edge device integration, support for more classes, improved accuracy with advanced models, cloud-based processing.

In essence, the study demonstrates a scalable, high-performance real-time object detection system using YOLOv8, addressing limitations of traditional methods and enabling practical deployment in dynamic live video environments.

Conclusion

This research paper presented a deep learning-based multi-classification detection system for live video streams. The proposed approach successfully detects and classifies multiple objects in real time with high accuracy and low latency. Experimental results validate the effectiveness of the system, making it suitable for various real-world applications. Future improvements can further enhance system performance and scalability.

References

[1] Ultralytics Documentation. [2] YOLO model documentation. [3] OpenCV Documentation.

Copyright

Copyright © 2026 Rishikesh Kumar Kushwaha, Tanmay Yadav, Mr. Abhishek Shahi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.