TCAS: A multiclass object detector for robot and computer vision applications

Abstract

Building efficient object detection systems is an important goal of computer and robot vision. If several object types are to be detected, the most simple solution is to run several object-specific classifiers independently of each other (in parallel). This solution is computationally expensive if several object classes are to be detected. In this paper, TCAS, a new classifier structure designed to be used on multiclass object detection problems is introduced as an alternative solution. TCAS offers an efficient solution and reduces the aggregated false detection rate. TCAS extends cascade classifiers (introduced by Viola & Jones) to the multiclass case and corresponds to a nested coarse-to-fine tree of multiclass nested boosted cascades. Results for three different object detection problems are presented: face and hand detection, robot detection, and multiview face detection. In the experiments, the obtained TCAS have classification times about 2-times shorter than the ones obtained using parallel cascades, and have the same or lower number of false positives (for the same detection rate). © 2012 Springer-Verlag.