Real-Time Pedestrian Detection With Deep Networks Cascades

Anelia Angelova, Alex Krizhevsky, Vincent Vanhoucke, Abhijit Ogale, Dave Ferguson
Google Research, 1600 Amphitheatre Parkway, Mountain View, CA, USA
26th British Machine Vision Conference (BMVC), 2015

   title={Real-Time Pedestrian Detection With Deep Networks Cascades},

   author={Anelia Angelova and Alex Krizhevsky and Vincent Vanhoucke and Abhijit Ogale and Dave Ferguson},


   booktitle={Proceedings of BMVC 2015}


Download Download (PDF)   View View   Source Source   Source codes Source codes




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.
VN:F [1.9.22_1171]
Rating: 5.0/5 (1 vote cast)
Real-Time Pedestrian Detection With Deep Networks Cascades, 5.0 out of 5 based on 1 rating

* * *

* * *

TwitterAPIExchange Object
    [oauth_access_token:TwitterAPIExchange:private] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
    [oauth_access_token_secret:TwitterAPIExchange:private] => o29ji3VLVmB6jASMqY8G7QZDCrdFmoTvCDNNUlb7s
    [consumer_key:TwitterAPIExchange:private] => TdQb63pho0ak9VevwMWpEgXAE
    [consumer_secret:TwitterAPIExchange:private] => Uq4rWz7nUnH1y6ab6uQ9xMk0KLcDrmckneEMdlq6G5E0jlQCFx
    [postfields:TwitterAPIExchange:private] => 
    [getfield:TwitterAPIExchange:private] => ?cursor=-1&screen_name=hgpu&skip_status=true&include_user_entities=false
    [oauth:protected] => Array
            [oauth_consumer_key] => TdQb63pho0ak9VevwMWpEgXAE
            [oauth_nonce] => 1477621859
            [oauth_signature_method] => HMAC-SHA1
            [oauth_token] => 301967669-yDz6MrfyJFFsH1DVvrw5Xb9phx2d0DSOFuLehBGh
            [oauth_timestamp] => 1477621859
            [oauth_version] => 1.0
            [cursor] => -1
            [screen_name] => hgpu
            [skip_status] => true
            [include_user_entities] => false
            [oauth_signature] => Hn9jj9OKX2f4nfe21cQD0LR6yes=

    [url] => https://api.twitter.com/1.1/users/show.json
Follow us on Facebook
Follow us on Twitter

HGPU group

2036 peoples are following HGPU @twitter

HGPU group © 2010-2016 hgpu.org

All rights belong to the respective authors

Contact us: