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2017 | OriginalPaper | Buchkapitel

Cascade Classifiers and Saliency Maps Based People Detection

verfasst von : Wilbert G. Aguilar, Marco A. Luna, Julio F. Moya, Vanessa Abad, Hugo Ruiz, Humberto Parra, William Lopez

Erschienen in: Augmented Reality, Virtual Reality, and Computer Graphics

Verlag: Springer International Publishing

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Abstract

In this paper, we propose algorithm and dataset for pedestrian detection focused on HCI and Augmented Reality applications. We combine cascade classifiers with saliency maps for improving the performance of the detectors. We train a HAAR-LBP and HOG cascade classifier and introduce CICTE_PeopleDetection dataset with images from surveillance cameras at different angles and altitudes. Our algorithm performance is compared with other approaches from the state of art. In the results, we can see that cascade classifiers with saliency maps improve the performance of pedestrian detection due to the rejection of false positives in the image.

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Titel: Cascade Classifiers and Saliency Maps Based People Detection
verfasst von: Wilbert G. Aguilar
Marco A. Luna
Julio F. Moya
Vanessa Abad
Hugo Ruiz
Humberto Parra
William Lopez
Verlag: Springer International Publishing
Buch: Augmented Reality, Virtual Reality, and Computer Graphics
Print ISBN: 978-3-319-60927-0

Electronic ISBN: 978-3-319-60928-7

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-60928-7_42

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