[en] Human detection in video streams is an important task in many applications including video surveillance. Surprisingly, only few papers have been devoted to this topic.
This paper presents a new approach to detect humans in video streams. Our approach is based on the temporal information present in videos. A background subtraction algorithm is first used to segment the silhouettes of the users and the moving objects. Then a classification process in two steps determines for each connected component if it corresponds to the silhouette of a human or not. During the first step, a probabilistic information is computed for each pixel independently. The information from a subset of pixels is then gathered to predict the class of the observed silhouette.
This paper presents the principles and some results obtained on real silhouettes. It is shown that our approach is efficient for the detection of humans in video streams.
Research Center/Unit :
Intelsig Telim
Disciplines :
Electrical & electronics engineering
Author, co-author :
Pierard, Sébastien ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Lejeune, Antoine ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Exploitation des signaux et images
Van Droogenbroeck, Marc ; Université de Liège - ULiège > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications
Language :
English
Title :
A probabilistic pixel-based approach to detect humans in video streams
Publication date :
May 2011
Event name :
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2011)
Event organizer :
IEEE
Event place :
Prague, Czechia
Event date :
from 22-05-2011 to 27-05-2011
Audience :
International
Main work title :
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2011)
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Bibliography
N. Dalal and B. Triggs, "Hog, histograms of oriented gradients for human detection," in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), San Diego, USA, June 2005, vol. 1, pp. 886-893.
Q. Zhu, M.-C. Yeh, K.-T. Cheng, and S. Avidan, "Fast human detection using a cascade of histograms of oriented gradients," in IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), New York, USA, June 2006, vol. 2, pp. 1491-1498.
O. Barnich, S. Jodogne, and M. Van Droogenbroeck, "Robust analysis of silhouettes by morphological size distributions," in Advanced Concepts for Intelligent Vision Systems (ACIVS 2006). September 2006, vol. 4179 of Lecture Notes on Computer Science, pp. 734-745, Springer.
O. Barnich, Motion detection and human recognition in video sequences, Ph.D. thesis, University of Liège, Belgium, September 2010.
S. Piérard, V. Pierlot, O. Barnich, M. Van Droogenbroeck, and J. Verly, "A platform for the fast interpretation of movements and localization of users in 3D applications driven by a range camera," in 3DTV Conference, Tampere, Finland, June 2010.
M. Hu, "Visual pattern recognition by moment invariants," IRE Transactions on Information Theory, vol. 8, pp. 179-187, 1962.
R. Díaz de León and L. Sucar, "Human silhouette recognition with Fourier descriptors," in IEEE International Conference on Pattern Recognition (ICPR), Barcelona, Spain, September 2000, vol. 3, pp. 709-712, IEEE Computer Society.
P. Geurts, D. Ernst, and L. Wehenkel, "Extremely randomized trees," Machine Learning, vol. 63, no. 1, pp. 3-42, April 2006.
R. Marée, Classification automatique d'images par arbres de décision, Ph.D. thesis, University of Liège, Belgium, February 2005.
S. Piérard and M. Van Droogenbroeck, "A technique for building databases of annotated and realistic human silhouettes based on an avatar," in Workshop on Circuits, Systems and Signal Processing (ProRISC), Veldhoven, The Netherlands, November 2009, pp. 243-246.
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