Abstract

Building robust and fast object detection systems is an important goal of computer vision. A problem arises when several object types are to be detected, because the computational burden of running several specific classifiers in parallel becomes a problem. In addition the accuracy and the training time can be greatly affected. Seeking to provide a solution to these problems, we extend cascade classifiers to the multiclass case by proposing the use of multiclass coarse-to-fine (CTF) nested cascades. The presented results show that the proposed system scales well with the number of classes, both at training and running time. © 2010 IEEE.