C++ and vehicle detection in computer vision

04 Oct 2023

Computer vision is a fascinating field that deals with processing and analyzing digital images and videos to extract meaningful information. One popular application of computer vision is vehicle detection, which involves identifying and tracking vehicles in real-time. In this blog post, we’ll explore how to implement vehicle detection using C++.

Introduction to Vehicle Detection
Image Preprocessing
Feature Extraction
Training the Classifier
Object Detection
Conclusion

1. Introduction to Vehicle Detection

Vehicle detection is an essential component in various applications, such as advanced driver assistance systems, autonomous vehicles, and traffic surveillance. The goal is to accurately and efficiently detect vehicles in images or videos, allowing for further analysis or decision-making.

2. Image Preprocessing

Before applying vehicle detection techniques, it is crucial to preprocess the input images. This step typically involves operations like resizing, scaling, and normalization to enhance the image quality and reduce noise.

#include <opencv2/opencv.hpp>

// Load and preprocess the input image
cv::Mat image = cv::imread("input.jpg");
cv::resize(image, image, cv::Size(640, 480)); // Resize to a fixed size
cv::cvtColor(image, image, cv::COLOR_BGR2GRAY); // Convert to grayscale
cv::GaussianBlur(image, image, cv::Size(5, 5), 0); // Apply Gaussian blur
cv::equalizeHist(image, image); // Enhance contrast using histogram equalization

3. Feature Extraction

To distinguish vehicles from other objects in the image, we need to extract relevant features that capture the characteristics of vehicles. Common features used in vehicle detection include Histogram of Oriented Gradients (HOG) features and Haar-like features.

#include <opencv2/objdetect.hpp>

// Load pre-trained Haar cascade classifier
cv::CascadeClassifier cascade;
cascade.load("haarcascade_car.xml");

// Detect vehicles using Haar-like features
std::vector<cv::Rect> vehicles;
cascade.detectMultiScale(image, vehicles, 1.1, 3, 0 | cv::CASCADE_SCALE_IMAGE, cv::Size(30, 30));

4. Training the Classifier

To achieve optimal performance, training a machine learning classifier on a large dataset of positive and negative samples is essential. This step typically involves collecting annotated vehicle images and training a classifier, such as a Support Vector Machine (SVM) or Convolutional Neural Network (CNN).

#include <opencv2/ml.hpp>

// Load training data and labels
cv::Mat trainingData; // Feature vectors
cv::Mat labels; // Binary labels (0 for non-vehicles, 1 for vehicles)

// Train the classifier using SVM or other algorithms
cv::Ptr<cv::ml::SVM> svm = cv::ml::SVM::create();
svm->train(trainingData, cv::ml::ROW_SAMPLE, labels);

5. Object Detection

Once the classifier is trained, we can apply it to detect vehicles in new images or videos. This involves sliding a window over the input image and classifying each window as a vehicle or non-vehicle based on the learned model.

// Sliding window approach for object detection
for (int y = 0; y < image.rows - windowSize.height; y += windowStride.y) {
    for (int x = 0; x < image.cols - windowSize.width; x += windowStride.x) {
        cv::Rect window(x, y, windowSize.width, windowSize.height);
        
        // Extract features from the window
        cv::Mat featureVector = extractFeatures(window); // Custom function
        
        // Classify the window using the trained classifier
        float prediction = svm->predict(featureVector);
        
        // Check if the window contains a vehicle
        if (prediction == 1) {
            cv::rectangle(image, window, cv::Scalar(0, 255, 0), 2); // Draw bounding box
        }
    }
}

6. Conclusion

Vehicle detection in computer vision is an exciting and challenging task. Using C++ and various computer vision techniques, we can develop robust and efficient vehicle detection systems. By preprocessing images, extracting key features, training classifiers, and applying object detection algorithms, we can accurately detect vehicles in real-world scenarios.

Start building your own vehicle detection system today and explore the endless possibilities of computer vision!

#computerVision #vehicleDetection

Note: The code snippets in this blog post are for illustrative purposes only. Actual implementation may require additional code and customization based on your specific requirements.

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