Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/s00138-010-0251-6 | Mach. Vis. Appl. |
Keywords | Field | DocType |
Dynamic texture,Detection,Optical flow,Background modelling,SVD,Photometric invariants,Temporal periodicity | Singular value decomposition,Residual,Computer vision,Singular value,Machine vision,Pattern recognition,Computer science,Temporal database,Artificial intelligence,Periodic graph (geometry),Optical flow | Journal |
Volume | Issue | ISSN |
22 | 5 | 0932-8092 |
Citations | PageRank | References |
10 | 0.70 | 24 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chetverikov, D. | 1 | 956 | 99.89 |
| Sándor Fazekas | 2 | 242 | 9.74 |
| Michal Haindl | 3 | 488 | 50.33 |