Object Detector DLM Web

Overview

Object-Detector-DLM-WEB is a web-based application that utilizes deep learning models for real-time object detection and tracking using your camera feed. The application is built using HTML, CSS, and JavaScript, and leverages TensorFlow.js with the COCO-SSD model to detect and label objects in the video stream.

Features

• Real-time object detection using TensorFlow.js and COCO-SSD model.
• Camera access to stream live video for detection.
• Display of detected object names and confidence scores.
• Responsive design with Google UI/UX styling.

Getting Started

Prerequisites

• A modern web browser that supports HTML5, CSS3, and JavaScript.
• Internet connection to load TensorFlow.js and COCO-SSD model from CDNs.

Installation

1. Clone the Repository:

   git clone https://github.com/harshitj183/Object-Detector-DLM-WEB.git

2. Navigate to the Project Directory:

   cd Object-Detector-DLM-WEB

3. Open index.html in Your Browser:

   You can open the index.html file directly in your browser to view and use the application.

Usage

1. Allow Camera Access:

   When you first load the application, you'll be prompted to allow access to your camera. Grant permission for the application to use the camera.

2. View Object Detection:

   Once camera access is granted, the application will start streaming video and detecting objects in real-time. The detected objects and their names will be displayed on the video feed.

Files

• index.html: The main HTML file that sets up the page structure.
• style.css: Contains the styling for the application, including Google UI/UX design.
• script.js: Contains the JavaScript code for handling video streaming, object detection, and drawing bounding boxes.

Contributing

If you would like to contribute to this project, please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Commit your changes and push to your fork.
4. Open a pull request with a description of your changes.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• TensorFlow.js for providing deep learning capabilities in JavaScript.
• COCO-SSD Model for object detection.
• Google UI/UX for design inspiration.

Contact

For any questions or feedback, please contact Harshit Jaiswal.