voc-release5

Object Detection with Discriminatively Trained Part Based Models Object recognition is one of the fundamental challenges in computer vision. In this paper we consider the problem of detecting and localizing generic objects from categories such as people or cars in static images. This is a difficult problem because objects in such categories can vary greatly in appearance. Variations arise not only from changes in illumination and viewpoint, but also due to non-rigid deformations, and intraclass variability in shape and other vis ual properties. For example, people wear different clothes and take a variety of poses while cars come in a various shapes and colors. ual properties. For example, people wear different clothes and take a variety of poses while cars come in a various shapes and colors.