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This project focuses on the study and implementation of various object detection techniques. It covers a wide range of objects including faces, vehicles, and more. Different methods are employed to achieve accurate and efficient detection.

The following object detection techniques have been explored in this project:

• Description:Face detection is a crucial component in many applications. This technique involves locating and identifying human faces within an image or video stream.

• Face Detection with dlib

  This project contains Python scripts for face detection using the dlib library with two

  different techniques: Histogram of Oriented Gradients (HOG) and Convolutional Neural Network (CNN).

  The scripts are designed to work with both images and videos, providing a flexible solution for face detection tasks.

The face detection scripts in this project leverage two distinct techniques for accurate and efficient face detection:

1. Histogram of Oriented Gradients (HOG)

   • Utilizes the HOG + SVM face detection algorithm provided by dlib.
   • Well-suited for real-time face detection in images and videos.
   • Provides a good balance between accuracy and speed.

   output

   • image

   

   • video

   

2. Convolutional Neural Network (CNN)

   • Uses the dlib CNN face detection model for improved accuracy.
   • Particularly effective when high accuracy is required, but it may be slower than HOG.
   • Suitable for scenarios where precision is crucial, such as image analysis.

   output

   • image

   

   • video

   

• Features

  • HOG-Based Face Detection: Efficient and real-time face detection using the HOG technique.

  • CNN-Based Face Detection: Improved accuracy through the use of a Convolutional Neural Network.

  • Image Detection: Detect faces in images and save the results.

  • Video Detection: Perform real-time face detection in videos and save the processed video.

• Description: vehicle detection is essential in scenarios like autonomous driving and traffic monitoring. This technique aims to identify and locate vehicles in images or video frames.

• Implementation:

  Techniques Used

  The following vehicle detection techniques have been explored in this project:

  1. YOLO (You Only Look Once)

  • Description: YOLO is a popular real-time object detection system that processes images in a single pass. It is known for its speed and accuracy in detecting objects, including vehicles.

  • Implementation:YOLO vehicle Detection Code

    • result

    

  2. Haar Cascade Classifier

  • Description: Haar Cascade Classifier is a machine learning object detection method used to identify objects in images or video streams. It can be trained to detect specific objects, including vehicles.

  • Implementation:Code

    • result