Reference : A probabilistic pixel-based approach to detect humans in video streams
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Electrical & electronics engineering
http://hdl.handle.net/2268/88198
A probabilistic pixel-based approach to detect humans in video streams
English
Pierard, Sébastien mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications >]
Lejeune, Antoine mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Exploitation des signaux et images >]
Van Droogenbroeck, Marc mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications >]
May-2011
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2011)
IEEE
921-924
Yes
No
International
International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2011)
from 22-05-2011 to 27-05-2011
IEEE
Prague
Czech Republic
[en] Human ; Detection ; Silhouette ; Background ; Background subtraction ; Classification ; Machine learning ; Matching ; Identification ; People ; Extraction ; Video ; Video streams ; Real-time ; Pedestrian ; Surveillance
[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.
Intelsig
Researchers ; Professionals ; Students ; General public ; Others
http://hdl.handle.net/2268/88198
10.1109/ICASSP.2011.5946555
http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5946555&url=http%3A%2F%2Fieeexplore.ieee.org%2Fstamp%2Fstamp.jsp%3Ftp%3D%26arnumber%3D5946555

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