https://github.com/vishal-038/real_time_object_detection

Object Detection in Real-Time Video Stream This project demonstrates the use of YOLOv3 (You Only Look Once version 3) for real-time object detection in video streams. YOLOv3 is a state-of-the-art deep learning model known for its balance between speed and accuracy in detecting objects within images and videos. Object Detection in Real-Time Video
https://github.com/vishal-038/real_time_object_detection

opencv python yolov3

Last synced: 18 days ago
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Object Detection in Real-Time Video Stream This project demonstrates the use of YOLOv3 (You Only Look Once version 3) for real-time object detection in video streams. YOLOv3 is a state-of-the-art deep learning model known for its balance between speed and accuracy in detecting objects within images and videos. Object Detection in Real-Time Video

• Host: GitHub
• URL: https://github.com/vishal-038/real_time_object_detection
• Owner: VISHAL-038
• Created: 2024-09-04T15:26:53.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-09-12T04:19:41.000Z (4 months ago)
• Last Synced: 2024-10-31T12:46:38.690Z (2 months ago)
• Topics: opencv, python, yolov3
• Language: Jupyter Notebook
• Homepage:
• Size: 123 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Real_Time_Object_Detection

YOLOv3: Object Detection in Real-Time Video Stream

This project demonstrates the use of YOLOv3 (You Only Look Once version 3) for real-time object detection in video streams. YOLOv3 is a state-of-the-art deep learning model known for its balance between speed and accuracy in detecting objects within images and videos.

Key Features

Real-Time Detection: Processes video frames quickly enough for real-time applications.

Multi-Class Detection: Detects multiple objects from a predefined set of classes (e.g., people, vehicles, animals).

High Accuracy: Provides accurate object localization and classification in each frame.

How It Works

Load the Model: The YOLOv3 model is loaded using pre-trained weights and configuration files. These files define the architecture and parameters of the model.

Prepare Video Stream: Captures frames from a video source (e.g., webcam, video file) for processing.

Preprocess the Frame: Each frame is resized and normalized to fit the input requirements of the YOLOv3 model.

Run Detection: The preprocessed frame is passed through the YOLOv3 network to obtain bounding boxes, class labels, and confidence scores for detected objects.

Postprocess and Display: Detected objects are highlighted with bounding boxes and labels, and the processed frame is displayed in a window to show real-time results.

Handle Output: Continuously processes video frames and updates the display until the user stops the stream.

Requirements

Python: Programming language used for the project.

OpenCV: Library for video capture, image processing, and display.

YOLOv3 Model Files:

YOLOv3 Weights

YOLOv3 Config

COCO Names

This project demonstrates how YOLOv3 can be integrated into real-time systems for various applications, such as surveillance, autonomous vehicles, and interactive video analysis.