Spatiotemporal bag-of-features for early wildfire smoke detection

Image and Vision Computing

Volumn 31, Issue 10, 2013, Pages 786-795

  Ko, Byoungchul ^a   Park, Junoh ^a   Nam, Jae Yeal ^a  

^a Keimyung University   (South Korea)

Author keywords

Bag of features; Histogram of oriented gradient; Histogram of oriented optical flow; Random forest; Spatiotemporal feature; Wildfire smoke detection

Indexed keywords

DECISION TREES; FIRES; OPTICAL FLOWS; PATTERN RECOGNITION; SMOKE; SMOKE DETECTORS;

BAG-OF-FEATURES; HISTOGRAM OF ORIENTED GRADIENTS; HISTOGRAM OF ORIENTED OPTICAL FLOWS; RANDOM FORESTS; SPATIO TEMPORAL FEATURES; WILDFIRE SMOKE;

GRAPHIC METHODS;

EID: 84883893239     PISSN: 02628856     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.imavis.2013.08.001     Document Type: Article

References (28)

1. C.C. Pierce, G.D. Ram, and D. Capistrano Lessons from Forest Decentralization: Money, Justice and the Quest for Good Governance in Asia-Pacific first ed. 2008 Earthscan London 101 115
2. B.C. Ko, and S.Y. Kwak A survey of computer vision-based natural disaster warning systems Opt. Eng. 57 1 2012 070901-1 070901-11
3. B.C. Ko, J.Y. Kwak, and J.Y. Nam Wildfire smoke detection using temporal-spatial features and random forest classifiers Opt. Eng. 51 1 2012 017208-1 017208-10
4. J. Vicente, and P. Guillemant An image processing technique for automatically detecting forest fire Int. J. Therm. Sci. 41 12 2002 1113 1120
5. D. Krstinić, D. Stipaničev, and T. Jakovčević Histogram-based smoke segmentation in forest fire detection system Inf. Technol. Control 38 3 2009 237 244
6. B.U. Töreyin, and A.E. Cetin Wildfire detection using LMS based active learning IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 2009 1461 1464
7. S.J. Ham, B.C. Ko, and J.Y. Nam Vision based forest smoke detection using analyzing of temporal patterns of smoke and their probability models SPIE Electronics Imaging Machine Vision Applications IV 2011 1 6
8. Y.H. Habiboglu, O. Gunay, and A.E. Cetin Real-time wildfire detection using correlation descriptors 19th European Signal Processing Conference (EUSIPCO) 2011 894 898
9. A. Genovese, R. Donida Labati, V. Piuri, and F. Scotti Wildfire smoke detection using computational intelligence techniques IEEE Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) 2011 1 6
10. A. Benazza-Benyahia, N. Hamouda, F. Tlili, and S. Ouerghi Early smoke detection in forest areas from DCT based compressed video 20th European Signal Processing Conference (EUSIPCO) 2012 2752 2756
11. M. Stula, D. Krstinic, and L. Seric Intelligent forest fire monitoring system Inf. Syst. Front. (ISF) 14 3 2012 725 739
12. J.O. Park, B.C. Ko, S.Y. Kwak, and J.Y. Nam Wildfire smoke detection using spatiotemporal bag-of-features of smoke IEEE Conference on Workshop on the Applications of Computer Vision (WACV) 2013
13. T. Jakovcevic, M. Braovic, D. Stipanicev, and D. Krstinic Review of wildfire smoke detection techniques based on visible spectrum video analysis Image and Signal Processing and Analysis (ISPA) 2011 480 484
14. B.C. Ko, J.W. Gim, and J.Y. Nam Automatic white blood cell segmentation using stepwise merging rules and gradient vector flow snake Micron 42 7 2011 695 705
15. E. Norwak, F. Jurie, and B. Triggs Sampling strategies of bag-of-features image classification European Conference on Computer Vision (ECCV) 2006 490 503 (Pubitemid 44029653)
16. R. Chaudhry, A. Ravichandran, G. Hager, and R. Vidal Histograms of oriented optical flow and Binet-Cauchy kernels on nonlinear dynamical systems for the recognition of human actions IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2009 1932 1939
17. M. Marszalek, C. Schmid, and B. Rozenfeld Learning realistic human actions from movies IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2008 1 8
18. J.C. Niebles, H. Wang, and L. Fei-Fei Unsupervised learning of human action categories using spatio-temporal words British Machine Vision Conference (BMVC) 2006 1 11
19. X. Yuan, J. Yu, Z. Qin, and T. Wan A sift-LBP image retrieval model based on bag-of-features IEEE International Conference on Image Processing (ICIP) 2011 1061 1064
20. Y.G. Jiang, C.W. Ngo, and J. Yang Towards optimal bag-of-features for object categorization and semantic video retrieval ACM International Conference on Image and Video Retrieval (CIVR) 2007 494 501 (Pubitemid 350229664)
21. I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld Learning realistic human actions from movies IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) 2008 1 8
22. G. Heidemann Unsupervised image categorization Image Vision Comput. 23 10 2005 861 876 (Pubitemid 41165392)
23. L. Breiman Random forests Mach. Learn. 45 1 2001 5 32 (Pubitemid 32933532)
24. F. Zhua, L. Shaoa, and M. Linb Multi-view action recognition using local similarity random forests and sensor fusion Pattern Recognit. Lett. 34 1 2013 20 24
25. D.Y. Kim, J.Y. Kwak, B.C. Ko, and J.Y. Nam Human detection using wavelet-based CS-LBP and a cascade of random forests IEEE International Conference on Multimedia and Expo (ICME) 2012 362 367
26. R. Maree, P. Geurts, and L. Wehenkel Content-based image retrieval by indexing random subwindows with randomized trees IPSJ Trans. Comput. Vis. Appl. 1 1 2009 46 57
27. B.C. Ko, S.H. Kim, and J.Y. Nam X-ray image classification using random forests with local wavelet-based CS-local binary patterns J. Digit. Imaging 24 6 2011 1141 1151
28. Q. Tian, and S. Zhang Descriptive visual words and visual phrases for image applications ACM Multimedia 2009 19 24