HOG + SVM for Object Detection (Face and Pedestrian)

This documentation explains the HOG (Histogram of Oriented Gradients) feature descriptor and SVM (Support Vector Machine) classifier, and how their combination is effectively used for object detection, particularly for faces and pedestrians.

1. Introduction to HOG and SVM

1.1. HOG (Histogram of Oriented Gradients)

HOG is a powerful feature descriptor widely used in computer vision for object detection. It works by capturing the distribution of gradient orientations (edge directions) within localized portions of an image. This approach is robust to illumination and shadowing variations.

1.2. SVM (Support Vector Machine)

SVM is a supervised machine learning algorithm primarily used for classification tasks. Its goal is to find an optimal hyperplane that best separates data points belonging to different classes, maximizing the margin between them.

2. Why Combine HOG + SVM?

The synergy between HOG and SVM creates an effective pipeline for detecting objects like humans (pedestrians) and faces. HOG extracts discriminative features from the image, and SVM utilizes these features to classify whether an object of interest is present in a given region.

3. HOG Feature Extraction Process

The HOG feature extraction involves several key steps:

1. Convert to Grayscale: The input image is converted to grayscale to simplify gradient calculation by removing color information.
2. Compute Gradients: For each pixel, the horizontal (Gx) and vertical (Gy) gradients are computed. These indicate the intensity changes and the direction of edges.
   • Gradient Magnitude: $M = \sqrt{Gx^2 + Gy^2}$
   • Gradient Orientation: $\theta = \arctan\left(\frac{Gy}{Gx}\right)$
3. Divide Image into Cells: The image is divided into small, non-overlapping rectangular cells (e.g., $8 \times 8$ pixels).
4. Create Cell Histograms: For each cell, a histogram of gradient orientations is computed. This histogram represents the distribution of edge directions within that cell. Typically, the orientation range is divided into a fixed number of bins (e.g., 9 bins covering $0^\circ$ to $180^\circ$).
5. Group Cells into Blocks: Cells are grouped into larger, overlapping blocks (e.g., $2 \times 2$ cells).
6. Normalize Block Histograms: To achieve illumination and contrast invariance, histograms within each block are normalized. This is often done using an L2-norm.
7. Concatenate Features: The normalized histograms from all blocks are concatenated to form a single feature vector for the entire detection window.

4. SVM Classification Process

Once the HOG features are extracted for a potential object region (e.g., a sliding window over the image), the SVM classifier takes over:

1. Input: The HOG feature vector is fed into the SVM.
2. Classification: The SVM uses a decision boundary (linear or non-linear, like RBF kernel) to classify the input feature vector.
3. Training: The SVM is trained on a dataset containing labeled examples of the target object (positive examples, e.g., images of pedestrians or faces) and non-objects (negative examples, e.g., background images). The training process learns a model that can accurately distinguish between these classes.

5. Python Example: Pedestrian Detection with OpenCV

This example demonstrates how to use OpenCV's pre-trained HOG detector for pedestrian detection in a video.

import cv2

# Initialize the HOG descriptor
hog = cv2.HOGDescriptor()

# Set the default people detector (trained HOG + SVM)
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())

# Open the video file
cap = cv2.VideoCapture('video.mp4')

# Check if video opened successfully
if not cap.isOpened():
    print("Error: Could not open video file.")
    exit()

while cap.isOpened():
    # Read a frame from the video
    ret, frame = cap.read()

    # If frame is not read correctly, break the loop
    if not ret:
        break

    # Resize frame for faster processing (optional)
    frame = cv2.resize(frame, (640, 480))

    # Detect people in the frame
    # winStride: step size for sliding window, typically small
    # padding: adds padding to the image to detect objects near borders
    boxes, weights = hog.detectMultiScale(frame, winStride=(8, 8))

    # Draw bounding boxes around detected people
    for (x, y, w, h) in boxes:
        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2) # Green rectangle

    # Display the resulting frame
    cv2.imshow("Pedestrian Detection", frame)

    # Break the loop if 'ESC' key is pressed
    if cv2.waitKey(1) == 27:
        break

# Release the video capture object and close all windows
cap.release()
cv2.destroyAllWindows()

6. Notes for Face Detection

While HOG + SVM can be applied to face detection, modern state-of-the-art systems often leverage deep learning models (like Convolutional Neural Networks) for higher accuracy. However, for academic projects, lightweight systems, or specific use cases, you can train a custom SVM classifier using HOG features extracted from face datasets like Labeled Faces in the Wild (LFW).

7. Applications

The HOG + SVM approach has several practical applications:

• Pedestrian Detection: Crucial for autonomous vehicles and driver-assistance systems.
• Human Detection: Used in surveillance systems for monitoring and security.
• Face Detection: Can be employed in real-time video analysis and image processing.
• Crowd Analysis: Monitoring crowd density and behavior.

8. Pros and Cons

9. Related SEO Keywords

• HOG feature descriptor
• SVM classification algorithm
• HOG + SVM pedestrian detection
• Object detection using HOG
• OpenCV HOG people detector
• Support Vector Machine explained
• Human detection in video
• Face detection with HOG SVM
• Gradient orientation histogram
• Lightweight object detection models

10. Interview Questions

• What is the Histogram of Oriented Gradients (HOG) and how does it work?
• How does a Support Vector Machine (SVM) classify data?
• Why is HOG commonly combined with SVM for object detection?
• Explain the process of extracting HOG features from an image.
• How are gradients computed and used in HOG?
• What are the advantages and limitations of using HOG + SVM compared to deep learning methods?
• How does the HOG + SVM pipeline perform in pedestrian detection?
• Can HOG + SVM be used for face detection? If so, how would you approach it?
• What role do cells and blocks play in the HOG feature extraction process?
• How would you improve the accuracy of a HOG + SVM based detection system?