基于hog的特征目标自动检测算法 MATLAB代码

以下是基于HOG特征的目标自动检测算法的MATLAB代码示例：

首先，需要加载训练好的SVM分类器并且设置HOG特征的参数：

% Load trained SVM classifier
load('svm_classifier.mat');

% Set HOG parameters
cellSize = [8 8];
numBins = 9;
blockSize = [2 2];
blockOverlap = [1 1];
hog = vision.HistogramOfOrientedGradients('NumBins', numBins, 'BlockSize', blockSize, 'CellSize', cellSize, 'BlockOverlap', blockOverlap);

接着，读取待检测的图像，并且将其大小缩小为原来的1/2：

% Read input image
I = imread('test_image.jpg');

% Resize image
I = imresize(I, 0.5);

然后，使用滑动窗口技术，对图像进行分块，并且计算每个块的HOG特征描述子：

% Set window size and step size
winSize = [128 64];
stepSize = [8 8];

% Initialize bounding box list
bboxList = [];

% Iterate over image blocks
for row = 1:stepSize(1):size(I, 1)-winSize(1)
    for col = 1:stepSize(2):size(I, 2)-winSize(2)
        % Extract block
        block = I(row:row+winSize(1)-1, col:col+winSize(2)-1, :);
        
        % Compute HOG descriptor
        hogFeature = hog(block);
        
        % Classify block using SVM classifier
        label = predict(svm_classifier, hogFeature');
        
        % If block is positive, add bounding box to list
        if label == 1
            bboxList = [bboxList; col row winSize(2) winSize(1)];
        end
    end
end

最后，将所有的检测到的目标区域用矩形框标出来：

% Draw bounding boxes on image
for i = 1:size(bboxList, 1)
    rectangle('Position', bboxList(i,:), 'EdgeColor', 'r', 'LineWidth', 2);
end

% Display image with bounding boxes
figure;
imshow(I);

完整的代码如下所示：

% Load trained SVM classifier
load('svm_classifier.mat');

% Set HOG parameters
cellSize = [8 8];
numBins = 9;
blockSize = [2 2];
blockOverlap = [1 1];
hog = vision.HistogramOfOrientedGradients('NumBins', numBins, 'BlockSize', blockSize, 'CellSize', cellSize, 'BlockOverlap', blockOverlap);

% Read input image
I = imread('test_image.jpg');

% Resize image
I = imresize(I, 0.5);

% Set window size and step size
winSize = [128 64];
stepSize = [8 8];

% Initialize bounding box list
bboxList = [];

% Iterate over image blocks
for row = 1:stepSize(1):size(I, 1)-winSize(1)
    for col = 1:stepSize(2):size(I, 2)-winSize(2)
        % Extract block
        block = I(row:row+winSize(1)-1, col:col+winSize(2)-1, :);
        
        % Compute HOG descriptor
        hogFeature = hog(block);
        
        % Classify block using SVM classifier
        label = predict(svm_classifier, hogFeature');
        
        % If block is positive, add bounding box to list
        if label == 1
            bboxList = [bboxList; col row winSize(2) winSize(1)];
        end
    end
end

% Draw bounding boxes on image
for i = 1:size(bboxList, 1)
    rectangle('Position', bboxList(i,:), 'EdgeColor', 'r', 'LineWidth', 2);
end

% Display image with bounding boxes
figure;
imshow(I);