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
[21]  
Hull D., 2005, JOINT IEEE WORKSH VI, P117
[22]  
*INT CORP, 2007, MICR RES LAB OPENCV
[23]  
Kahl F, 2004, LECT NOTES COMPUT SC, V3247, P117
[24]   Robust method for periodicity detection and characterization of irregular cyclical series in terms of embedded periodic components [J].
Kanjilal, PP ;
Bhattacharya, J ;
Saha, G .
PHYSICAL REVIEW E, 1999, 59 (04) :4013-4025
[25]  
Lucas B. D., 1981, P INT JOINT C ART IN, P674, DOI DOI 10.5555/1623264.1623280
[26]  
Mileva Y, 2007, LECT NOTES COMPUT SC, V4713, P152
[27]  
Monnet A, 2003, NINTH IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOLS I AND II, PROCEEDINGS, P1305
[28]  
*MUSCLE NETW EXC, 2005, MULT UND SEM COMP LE
[29]   THRESHOLD SELECTION METHOD FROM GRAY-LEVEL HISTOGRAMS [J].
OTSU, N .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1979, 9 (01) :62-66
[30]  
PETERI R, 2005, DYNTEX COMPREHENSIVE