OpenCV Pedestrian Detection Application Solution Based on MYIR's T527 Development Board
2025-06-17
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In this article, we will introduce the testing of OpenCV pedestrian detection solution based on MYIR's MYD-LT527 Development Board (based on Allwinner T527 processor).
MYD-LT527 Development Board Top-view
MYD-LT527 Development Board Bottom-view
1. Software Environment Installation
1.1 Install OpenCV
sudo apt-get install libopencv-dev python3-opencv
1.2 Install pip
sudo apt-get install python3-pip
2. Introduction to Pedestrian Detection
The key steps in building the pedestrian detector using HOG and SVM include:
Prepare Training Dataset: the training dataset should contain large number of positive samples (pedestrian images) and negative samples (non-pedestrian images).
Compute HOG Feature: for each image, compute HOG feature. HOG feature is a one-dimensional vector where each element represents the gradient intensity at a specific location and direction in the image.
Train SVM Classifier: using the HOG feature as input, train the SVM classifier which will learn to distinguish between pedestrians and non-pedestrians.
Evaluate the Model: evaluate the trained model using the test dataset. Calculate metrics such as accuracy, recall and F1 score of the model.
3. Code Implementation
import cv2
import time
def detect(image,scale):
imagex=image.copy( ) # Make a copy of the function internally so that each function runs on a different image;
hog = cv2.HOGDescriptor( ) # Initialization direction gradient histogram description;
#Set up the SVM as a pre-trained pedestrian detector;
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector( ));
# Invoke the function detectMultiScale to detect the corresponding border of the pedestrian;
time_start=time.time( );# Record start time;
# Access (rectangular boxes corresponding to pedestrians, corresponding weights);
(rects, weights) = hog.detectMultiScale(imagex,scale=scale);
time_end = time.time( ) # Record end time;
# Draw each rectangular box;
for(x, y, w, h) in rects:
cv2.rectangle(imagex, (x, y), (x+w, y+h), (0, 0, 255), 2)
print("sacle size:", scale, ",time:", time_end-time_start)
name=str(scale)
cv2.imshow(name; imagex) # Show original effect
image = cv2.imread("back.jpg")
detect(image,1.01)
detect(image,1.05)
detect(image,1.3)
cv2.waitKey(0)
cv2.destroyAllWindows( )
4. Practical Operations
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