@article{ref1,
title="Detecting pedestrians on a Movement Feature Space",
journal="Pattern recognition",
year="2014",
author="Negri, Pablo and Goussies, Norberto and Lotito, Pablo",
volume="47",
number="1",
pages="56-71",
abstract="This work aims at detecting pedestrians in surveillance video sequences. A pre-processing step detects motion regions on the image using a scene background model based on level lines, which generates a Movement Feature Space, and a family of oriented histogram descriptors. A cascade of boosted classifiers generates pedestrian hypotheses using this feature space. Then, a linear Support Vector Machine validates the hypotheses that are likeliest to contain a person. The combination of the three detection phases reduces false positives, preserving the majority of pedestrians. The system tests conducted in our dataset, which contain low-resolution pedestrians, achieved a maximum performance of 25.5% miss rate with a rate of 10(-1) false positives per image. This value is comparable to the best detection values for this kind of images. In addition, the processing time is between 2 and 6 fps on 640 x 480 pixel captures. This is therefore a fast and reliable pedestrian detector. (C) 2013 Elsevier Ltd. All rights reserved.<p /> <p>Language: en</p>",
language="en",
issn="0031-3203",
doi="10.1016/j.patcog.2013.05.020",
url="http://dx.doi.org/10.1016/j.patcog.2013.05.020"
}