Wildfire smoke detection using spatiotemporal bag-of-features of smoke

Proceedings of IEEE Workshop on Applications of Computer Vision

Volumn , Issue , 2013, Pages 200-205

  Park, Junoh ^a   Ko, Byoungchul ^a   Nam, Jae Yeal ^a   Kwak, Sooyeong ^b  

^a Keimyung University   (South Korea)

^b Hanbat National University   (South Korea)

Author keywords

[No Author keywords available]

Indexed keywords

DETECTION ACCURACY; DIFFUSION DIRECTION; HISTOGRAM OF GRADIENTS (HOG); HISTOGRAM OF OPTICAL FLOW (HOF); RANDOM FOREST CLASSIFIER; SPATIO TEMPORAL FEATURES; TEMPORAL FEATURES; THERMAL CONVECTIONS;

DECISION TREES; FIRES; SMOKE; SMOKE DETECTORS;

GRAPHIC METHODS;

EID: 84875584647     PISSN: 21583978     EISSN: 21583986     Source Type: Conference Proceeding    
DOI: 10.1109/WACV.2013.6475019     Document Type: Conference Paper

References (17)

1. B. C. Ko and S. Y. Kwak. A survey of computer vision-based natural disaster warning systems. Optical Engineering, 57(1):070901-1-070901-11, 2012.
2. D. Krstini?, D. Stipani?ev, and T. Jakov?evi?. Histogram-based smoke segmentation in forest fire detection system. Information Technology and Control, 38(3):237-244, 2009.
3. B. U. Töreyin and A. E. Cetin. Wildfire detection using LMS based active learning. IEEE International Conference on Acoustics, Speech and Signal Processing, 1461-1464, 2009.
4. 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. Image Processing: Machine Vision Applications IV, 7877:1-6, 2011.
5. Y. H. Habiboglu, O. Gunay, and A. E. Cetin. Real-time wildfire detection using correlation descriptors. 19th European Signal Processing Conference, 894-898, 2011.
6. B. C. Ko, J. Y. Kwak, and J. Y. Nam. Wildfire smoke detection using temporal-spatial features and random forest classifiers. Optical Engineering, 51(1):017208-1-017208-10, 2012.
7. Q. Tian and S. Zhang. Descriptive visual words and visual phrases for image applications. ACM Multimedia, 19-24, 2009.
8. 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, 42:695-705, 2011.
9. M. Marszalek, C. Schmid, and B. Rozenfeld. Learning realistic human actions from movies. IEEE Conference on Computer Vision and Pattern Recognition, 1-8, 2008.
10. P. Dollar, V. Rabaud, G. Cottrell, and S. Belongie. Behavior recognition via sparse spatio-temporal features. IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance, 65-72, 2005.
11. J. C. Niebles, H. Wang, and L. Fei-Fei. Unsupervised learning of human action categories using spatio-temporal words. British Machine Vision Conference, 1-11, 2006.
12. Y. G. Jiang C. W. Ngo, and J. Yang. Towards optimal bag-of-features for object categorization and semantic video retrieval. 6th ACM International Conference on Image and Video Retrieval, 494-501, 2007.
13. I. Laptev, M. Marszalek, C. Schmid, and B. Rozenfeld. Learining realistic human actions from movies. IEEE International Conference on Computer Vision and Pattern Recognition, 1-8, 2008.
14. A. Agarwal and B. Triggs. Hyperfetures-multilevel local coding for visual recognition. IEEE International Conference on Computer Vision and Pattern Recognition, 30-43, 2006.
15. L. Breiman. Random forests. Machine Learning, 45:5-32, 2001.
16. Actual Author Name..". The frobnicatable foo filter, 2006. Face and Gesture apper ID 324.
17. Authors. Frobnication tutorial, 2006. Some URL al tr.pdf.