Latent SVM is a detector that uses HOG features and a star-structured, part-based model consisting of a root filter and a set of part filters to represent an object category. HOGs are feature descriptors that are obtained by counting the occurrences of gradient orientations in localized portions of an image. On the other hand, a variant of support vector machines (SVM) classifiers are used in this detector to train models using partially labeled data. The basic idea of an SVM is constructing a hyperplane or set of hyperplanes in high-dimensional space. These hyperplanes are obtained to have the largest distance to the nearest training data point (functional margin in order to achieve low generalization errors). Like cascade detectors, Latent SVM uses a sliding window with different initial positions and scales where the algorithm is applied in order to detect if there is an object inside.

One of the advantages of the OpenCV Latent SVM implementation is that it allows the detection...