Combining face detection and people tracking in video sequences

Abstract : Face detection algorithms are widely used in computer vision as they provide fast and reliable results depending on the application domain. A multi view approach is here presented to detect frontal and profile pose of people face using Histogram of Oriented Gradients, i.e. HOG, features. A K-mean clustering technique is used in a cascade of HOG feature classifiers to detect faces. The evaluation of the algorithm shows similar performance in terms of detection rate as state of the art algorithms. Moreover, unlike state of the art algorithms, our system can be quickly trained before detection is possible. Performance is considerably increased in terms of lower computational cost and lower false detection rate when combined with motion constraint given by moving objects in video sequences. The detected HOG features are integrated within a tracking framework and allow reliable face tracking results in several tested surveillance video sequences.
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https://hal.inria.fr/inria-00502749
Contributor : Etienne Corvee <>
Submitted on : Thursday, July 15, 2010 - 3:53:47 PM
Last modification on : Tuesday, July 24, 2018 - 3:48:06 PM
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  • HAL Id : inria-00502749, version 1

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Etienne Corvee, François Bremond. Combining face detection and people tracking in video sequences. The 3rd International Conference on Imaging for Crime Detection and Prevention - ICDP09, Dec 2009, Kingston Upon Thames (London), United Kingdom. pp.1. ⟨inria-00502749⟩

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