Haar like and LBP based features for face, head and people detection in video sequences

Abstract : Actual computer vision algorithms cannot extract semantic information of people activity coming from the large and increasing amount of surveillance cameras installed around the world. Algorithms need to analyse video content at real time frame rate and with a false alarm detection rate as small as possible. Such algorithms can be dedicated and specifically parameterised in certain applications and restrained environment. To make algorithms as useful as possible, they need to tackle many challenging issues in order to correctly analyse human activties. For instance, people are rarely entirely seen in a video because of static (contextual object or they are partly seen by the camera field of view) and dynamic occlusion (e.g. person in front of another). We here present a novel people, head and face detection algorithm using Local Binary Pattern based features and Haar like features which we refer to as couple cell features. An Adaboost training scheme is adopted to train object features. During detection, integral images are used to speed up the process which can reach several frames per second in surveillance videos.
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Communication dans un congrès
International Workshop on Behaviour Analysis and Video Understanding (ICVS 2011), Sep 2011, Sophia Antipolis, France. pp.10, 2011
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Soumis le : vendredi 16 septembre 2011 - 16:40:40
Dernière modification le : samedi 27 janvier 2018 - 01:30:46
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  • HAL Id : inria-00624360, version 1

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Etienne Corvee, Francois Bremond. Haar like and LBP based features for face, head and people detection in video sequences. International Workshop on Behaviour Analysis and Video Understanding (ICVS 2011), Sep 2011, Sophia Antipolis, France. pp.10, 2011. 〈inria-00624360〉

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