Pedestrian Detection with a Large-Field-Of-View Deep Network
Venue
Proceedings of ICRA 2015
Publication Year
2015
Authors
Anelia Angelova, Alex Krizhevsky, Vincent Vanhoucke
BibTeX
Abstract
Pedestrian detection is of crucial importance to autonomous driving applications.
Methods based on deep learning have shown significant improvements in accuracy,
which makes them particularly suitable for applications, such as pedestrian
detection, where reducing miss rate is very important. Although they are accurate,
their runtime has been at best in seconds per image, which makes them not practical
for onboard applications. We present here a Large-Field-Of-View (LFOV) deep network
for pedestrian detection, that can achieve high accuracy and is designed to make
deep networks work faster for detection problems. The idea of the proposed
Large-Field-of-View deep network is to learn to make classification decisions
simultaneously and accurately at multiple locations. The LFOV network processes
larger image areas at much faster speeds than typical deep networks have been able
to do, and can intrinsically reuse computations. Our pedestrian detection solution,
which is a combination of a LFOV network and a standard deep network, works at 280
ms per image on GPU and achieves 35.85 average miss rate on the Caltech Pedestrian
Detection Benchmark.
