Authors
Anelia Angelova, Alex Krizhevsky, Vincent Vanhoucke
Publication date
2015/5/26
Conference
2015 IEEE International Conference on Robotics and Automation (ICRA)
Pages
704-711
Publisher
IEEE
Description
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 the 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 a Large-Field-Of-View (LFOV) deep network for pedestrian detection, that can ...
Methods based on deep learning have shown significant improvements in accuracy, which
makes them particularly suitable for applications, such as pedestrian detection, where
reducing the 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 a Large-Field-Of-View (LFOV) deep network for pedestrian detection, that can ...
Total citations
Scholar articles
A Angelova, A Krizhevsky, V Vanhoucke - 2015 IEEE International Conference on Robotics and …, 2015
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