HOG (Histogram of Oriented Gradients) in computer vision algorithms

introduction

Computer vision algorithms have achieved remarkable development in recent years, among which the HOG (Histogram of Oriented Gradients) algorithm has been widely used in target detection, pedestrian recognition, face recognition and other fields. This article will introduce the principle of HOG algorithm and its application in computer vision.

HOG algorithm principle

The HOG algorithm is a gradient-based feature description method that represents the characteristics of the image by calculating the gradient direction histogram of the local area in the image. The main steps are as follows:

1. Image preprocessing: Convert the input image into a grayscale image for subsequent gradient calculation.
2. Calculate gradient: Obtain the gradient field of the image by calculating the gradient magnitude and direction of each pixel.
3. Divide the image area: Divide the image into multiple small overlapping areas (cells), each area containing multiple pixels.
4. Calculate the gradient direction histogram: count the frequency distribution of the gradient direction for the pixels in each area to obtain the gradient direction histogram of the area.
5. Normalized histogram: Normalize the gradient direction histogram of each area to reduce the influence of factors such as lighting changes.
6. Feature vector generation: Combine the normalized histograms according to certain rules to generate the final feature vector.

The following is a sample code that uses the OpenCV library to implement the HOG algorithm:

pythonCopy codeimport cv2
import numpy as np
# 加载图像
image = cv2.imread("image.jpg")
# 创建HOG对象
hog = cv2.HOGDescriptor()
# 设置SVM分类器
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
# 对图像进行预处理
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# 检测行人
boxes, weights = hog.detectMultiScale(gray, winStride=(4, 4), padding=(8, 8), scale=1.05)
# 在图像中绘制检测结果
for (x, y, w, h) in boxes:
    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
# 显示图像
cv2.imshow("HOG Detection", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Please note that in the sample code ​image.jpg​is the path to the image file to be detected. This sample code uses the HOGDescriptor class provided by OpenCV to implement the HOG algorithm. First, ​cv2.HOGDescriptor()​create , and ​cv2.HOGDescriptor_getDefaultPeopleDetector()​set up the SVM classifier using . Then, the image is converted into a grayscale image and the pedestrian target is detected using ​hog.detectMultiScale()​the method . Finally, the detection results are displayed on the image by drawing a rectangular box.

Advantages of HOG algorithm

The HOG algorithm has the following advantages:

1. Not affected by image scale changes: The HOG algorithm uses local areas when calculating the gradient direction histogram, so it can effectively handle scale changes of the target in the image.
2. Robust to illumination changes: The HOG algorithm has less impact on illumination changes, and the impact of illumination changes on feature description can be reduced by normalizing the histogram.
3. Suitable for complex background environments: The HOG algorithm has a certain degree of robustness to background interference in images and can effectively extract the characteristics of the target.

Application of HOG algorithm

The HOG algorithm is widely used in the field of computer vision. The following are several common application scenarios:

1. Target detection: The HOG algorithm can be used to detect target objects in images, such as pedestrians, vehicles, etc. By calculating the feature vectors of different areas in the image, it can be determined whether there is a target object.
2. Face recognition: The HOG algorithm can be used to extract the feature vectors of face images, and face recognition can be achieved by comparing the feature vectors.
3. Pedestrian recognition: The HOG algorithm is widely used in the field of pedestrian recognition. It can determine the position and posture of pedestrians by extracting their feature vectors.

The following is a sample code that uses the scikit-image library to implement the HOG algorithm:

pythonCopy codefrom skimage import feature
from skimage import data
import matplotlib.pyplot as plt
# 加载图像
image = data.astronaut()
# 将图像转换为灰度图像
gray = data.astronaut()
# 计算HOG特征
hog_feature, hog_image = feature.hog(gray, visualize=True)
# 显示原始图像和HOG特征图像
fig, axes = plt.subplots(1, 2, figsize=(12, 6))
axes[0].imshow(image)
axes[0].set_title('Original Image')
axes[0].axis('off')
axes[1].imshow(hog_image, cmap=plt.cm.gray)
axes[1].set_title('HOG Feature')
axes[1].axis('off')
plt.show()

Please note that the image in the example code ​data.astronaut()​is a predefined example image that you can change to your own if needed. This sample code uses ​feature.hog()​the function to calculate the HOG features of the image. ​visualize=True​The HOG feature image can be generated using the parameters. Finally, the original image and HOG feature image are displayed through the Matplotlib library.

in conclusion

The HOG algorithm is a gradient-based feature description method that is widely used in the field of computer vision. It can effectively extract image features by calculating the gradient direction histogram of local areas in the image. Through the analysis of feature vectors, tasks such as target detection, face recognition, and pedestrian recognition can be achieved. With the rise of deep learning, the HOG algorithm may be replaced by more advanced algorithms in some fields, but as a classic image feature description method, the HOG algorithm still has important research and application value.