Real-Time Pedestrian Detection With Deep Network Cascades
Venue
Proceedings of BMVC 2015
Publication Year
2015
Authors
Anelia Angelova, Alex Krizhevsky, Vincent Vanhoucke, Abhijit Ogale, Dave Ferguson
BibTeX
Abstract
We present a new real-time approach to object detection that exploits the
efficiency of cascade classifiers with the accuracy of deep neural networks. Deep
networks have been shown to excel at classification tasks, and their ability to
operate on raw pixel input without the need to design special features is very
appealing. However, deep nets are notoriously slow at inference time. In this
paper, we propose an approach that cascades deep nets and fast features, that is
both extremely fast and extremely accurate. We apply it to the challenging task of
pedestrian detection. Our algorithm runs in real-time at 15 frames per second. The
resulting approach achieves a 26.2% average miss rate on the Caltech Pedestrian
detection benchmark, which is competitive with the very best reported results. It
is the first work we are aware of that achieves extremely high accuracy while
running in real-time.
