python hog svm
时间: 2023-10-05 18:13:16 浏览: 46
HOG (Histogram of Oriented Gradients) SVM (Support Vector Machine) is a popular method for object detection in computer vision.
HOG is a feature descriptor that captures the gradient information of an image. It divides the image into small cells and computes histograms of gradient orientations in each cell. These histograms are then concatenated to form a feature vector that represents the image.
SVM is a supervised learning algorithm that can be used for classification and regression tasks. In the context of object detection, SVM is used to train a classifier that can distinguish between object and non-object regions in an image.
To use HOG SVM for object detection, we first need to train the SVM classifier using a set of positive and negative images. Positive images contain the object of interest, while negative images do not. The HOG feature descriptor is computed for each image, and the SVM learns to distinguish between the two classes based on these features.
Once the SVM classifier is trained, we can use it to detect objects in new images by sliding a window over the image and computing the HOG features for each window. The SVM classifier is then used to classify each window as either object or non-object. The regions that are classified as objects are then combined to form the final detection result.
HOG SVM has been successfully used for various object detection tasks, including pedestrian detection, face detection, and vehicle detection.