Approaches to the optimization and parallelization of computations in the problem of detecting objects of different classes in the image

This paper considers the problem of object detection in static images. We describe a state-of-the-art method based on Latent SVM algorithm. A well-known approach to speed up calculations, the construction of cascade classifiers, is used. We describe a computational scheme that uses cascade modification of the original Latent SVM algorithm The issues of parallelization and performance optimization are discussed. We analyze the most timeconsuming parts of implementation, consider several parallelization schemes and aspects of their performance. The results of numerical experiments on PASCAL Visual Object Challenge 2007 image dataset are given.