ORBi: Pierard Sébastien - A probabilistic pixel-based approach to detect humans in video streams

Reference : A probabilistic pixel-based approach to detect humans in video streams


	Document type :	Scientific congresses and symposiums : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Electrical & electronics engineering
	To cite this reference:	http://hdl.handle.net/2268/88198

Title :	A probabilistic pixel-based approach to detect humans in video streams
Language :	English
Author, co-author :	Pierard, Sébastien [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications >]
	Lejeune, Antoine [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Exploitation des signaux et images >]
	Van Droogenbroeck, Marc [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Télécommunications >]
Publication date :	May-2011
Main document title :	International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2011)
Publisher :	IEEE
Pages :	921-924
On invitation :	No
Audience :	International
Event name :	International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2011)
Event date :	from 22-05-2011 to 27-05-2011
Event organizer :	IEEE
Event place (city) :	Prague
Event country :	Czech Republic
Keywords :	[en] Human ; Detection ; Silhouette ; Background ; Background subtraction ; Classification ; Machine learning ; Matching ; Identification ; People ; Extraction ; Video ; Video streams ; Real-time ; Pedestrian ; Surveillance
Abstract :	[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 units :	Intelsig
Target :	Researchers ; Professionals ; Students ; General public ; Others
Permalink :	http://hdl.handle.net/2268/88198

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