Name
Abstract | ||
|---|---|---|
Video fire detection system which uses a spatio-temporal co variance matrix of video data is proposed. This system divides the video into spatio-temporal blocks and computes co variance features extracted from these blocks to detect fire. Feature vectors taking advantage of both the spatial and the temporal characteristics of flame colored regions are classified using an SVM classifier which is trained and tested using video data containing flames and flame colored objects. Experimental results are presented. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICASSP.2011.5946857 | Acoustics, Speech and Signal Processing |
Keywords | Field | DocType |
covariance matrices,feature extraction,flames,image classification,image colour analysis,object detection,spatiotemporal phenomena,support vector machines,SVM classifier,covariance descriptors,feature vectors,features extraction,flame colored regions,flame detection,spatiotemporal blocks,video data,video fire detection system,covariance descriptors,fire detection,support vector machines | Computer vision,Object detection,Feature vector,Pattern recognition,Computer science,Support vector machine,Feature extraction,Flame detection,Artificial intelligence,Covariance matrix,Fire detection,Covariance | Conference |
ISSN | ISBN | Citations |
1520-6149 E-ISBN : 978-1-4577-0537-3 | 978-1-4577-0537-3 | 6 |
PageRank | References | Authors |
0.60 | 6 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yusuf Hakan Habiboglu | 1 | 54 | 3.06 |
| Osman Gunay | 2 | 42 | 3.47 |
| A. Enis Çetin | 3 | 871 | 118.56 |