Real-Time Pedestrian Detection With Deep Networks Cascades
Google Research, 1600 Amphitheatre Parkway, Mountain View, CA, USA
26th British Machine Vision Conference (BMVC), 2015
@inproceedings{43850,
title={Real-Time Pedestrian Detection With Deep Networks Cascades},
author={Anelia Angelova and Alex Krizhevsky and Vincent Vanhoucke and Abhijit Ogale and Dave Ferguson},
year={2015},
booktitle={Proceedings of BMVC 2015}
}
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.
August 6, 2015 by hgpu
