Dynamic texture as foreground and background

被引:21
作者
Dmitry Chetverikov [1 ]
Sandor Fazekas [1 ]
Haindl, Michal [2 ]
机构
[1] Comp & Automat Res Inst SZTAKI, Geometr Modelling & Comp Vis Anal Lab, Budapest, Hungary
[2] Acad Sci Czech Republ, Inst Informat Theory & Automat, CR-18208 Prague, Czech Republic
关键词
Dynamic texture; Detection; Optical flow; Background modelling; SVD; Photometric invariants; Temporal periodicity; REAL-TIME FIRE; FLOW;
D O I
10.1007/s00138-010-0251-6
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Depending on application, temporal texture can be viewed as either foreground or background. We address two related problems: finding regions of dynamic texture in a video and detecting moving targets in a dynamic texture. We propose efficient and fast methods for both cases. The methods can be potentially used in real-time applications of machine vision. First, we show how the optical flow residual can be used to find dynamic texture in video. The algorithm is a practical, real-time simplification of the sophisticated and powerful but time-consuming method (Fazekas et al. in Int J Comput Vis 82:48-63, 2009). We give numerous examples of detecting and segmenting fire, smoke, water and other dynamic textures in real-world videos acquired by static and moving cameras. Then we apply the singular value decomposition (SVD) to a temporal data window in a video to detect targets in dynamic texture via the residual of the largest singular value. For a dynamic background of low-temporal periodicity, such as water, no temporal periodicity analysis is needed. For a highly periodic background such as an escalator, we show that periodicity analysis can improve detection results. Applying the method proposed in Chetverikov and Fazekas (Proceedings of British machine vision conference, vol 1, pp 167-176, 2006), we find the temporal period and use the resonant SVD to detect moving targets against a time-periodic background.
引用
收藏
页码:741 / 750
页数:10
相关论文
共 39 条
[31]  
Press W. H., 2002, NUMERICAL RECIPES C
[32]   Image change detection algorithms: A systematic survey [J].
Radke, RJ ;
Andra, S ;
Al-Kofahi, O ;
Roysam, B .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2005, 14 (03) :294-307
[33]  
RAHMAN A, 2007, 6 ACM INT C IM VID R, P417
[34]  
Rahman A, 2008, STUD COMPUT INTELL, V96, P291
[35]   USING COLOR TO SEPARATE REFLECTION COMPONENTS [J].
SHAFER, SA .
COLOR RESEARCH AND APPLICATION, 1985, 10 (04) :210-218
[36]   Learning patterns of activity using real-time tracking [J].
Stauffer, C ;
Grimson, WEL .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2000, 22 (08) :747-757
[37]  
Toreyin B.U., 2005, 2005 13 EUR SIGN PRO, P1
[38]   Computer vision based method for real-time fire and flame detection [J].
Töreyin, BU ;
Dedeoglu, Y ;
Güdükbay, U ;
Çetin, AE .
PATTERN RECOGNITION LETTERS, 2006, 27 (01) :49-58
[39]   Segmenting foreground objects from a dynamic textured background via a robust Kalman filter [J].
Zhong, J ;
Sclaroff, S .
NINTH IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOLS I AND II, PROCEEDINGS, 2003, :44-50