Abstract
Fire detection, as an immediate response of fire accident to avoid great disaster, has attracted many researchers’ focuses. However, the existing methods cannot effectively exploit the comprehensive attribute of fire to give satisfying accuracy. In this paper, we design a multi-attribute based fire detection system which combines the fire’s color, geometric, and motion attributes to accurately detect the fire in complicated surveillance videos. For geometric attribute, we propose a descriptor of shape variation by combining contour moment and line detection. Furthermore, to utilize fire’s instantaneous motion character, we design a dense optical flow based descriptor as fire’s motion attribute. Finally, we build a fire detection video dataset as the benchmark, which contains 305 fire and non-fire videos, with 135 very challenging negative samples for fire detection. Experimental results on this benchmark demonstrate that the proposed approach greatly outperforms the state-of-the-art method with 92.30% accuracy and only 8.33% false positives.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Foggia, P., Saggese, A., Vento, M.: Real-time fire detection for video-surveillance applications using a combination of experts based on color, shape, and motion. IEEE Trans. Circuits Syst. Video Technol. 25(9), 1545–1556 (2015)
Dimitropoulos, K., Barmpoutis, P., Grammalidis, N.: Spatio-temporal flame modeling and dynamic texture analysis for automatic video-based fire detection. IEEE Trans. Circuits Syst. Video Technol. 25(2), 339–351 (2015)
Qi, X., Ebert, J.: A computer vision-based method for fire detection in color videos. Int. J. Imaging Robot. 2(S09), 22–34 (2009)
Çelik, T., Demirel, H.: Fire detection in video sequences using a generic color model. Fire Saf. J. 44(2), 147–158 (2009)
Ko, B.C., Cheong, K.H., Nam, J.Y.: Fire detection based on vision sensor and support vector machines. Fire Saf. J. 44(3), 322–329 (2009)
Borges, P.V.K., Izquierdo, E.: A probabilistic approach for vision-based fire detection in videos. IEEE Trans. Circuits Syst. Video Technol. 20(5), 721–731 (2010)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Demi, M.: Contour tracking with a spatio-temporal intensity moment. IEEE Trans. Pattern Anal. Mach. Intell. 38(6), 1141–1154 (2016)
Sun, X., Yao, H., Zhang, S., Li, D.: Non-rigid object contour tracking via a novel supervised level set model. IEEE Trans. Image Process. 24(11), 3386–3399 (2015)
Mueller, M., Karasev, P., Kolesov, I., Tannenbaum, A.: Optical flow estimation for flame detection in videos. IEEE Trans. Image Process. 22(7), 2786–2797 (2013)
Töreyin, B.U., Dedeoglu, Y., Güdükbay, U., Çetin, A.E.: Computer vision based method for real-time fire and flame detection. Pattern Recogn. Lett. 27(1), 49–58 (2006)
Liang, C., Juang, C.: Moving object classification using a combination of static appearance features and spatial and temporal entropy values of optical flows. IEEE Trans. Intell. Transp. Syst. 16(6), 3453–3464 (2015)
Sun, D., Roth, S., Black, M.J.: A quantitative analysis of current practices in optical flow estimation and the principles behind them. Int. J. Comput. Vis. 106(2), 115–137 (2014)
Kittler, J.: Combining classifiers: a theoretical framework. Pattern Anal. Appl. 1(1), 18–27 (1998)
Liu, W., Mei, T., Zhang, Y., Che, C., Luo, J.: Multi-task deep visual-semantic embedding for video thumbnail selection. In: CVPR, pp. 3707–3715 (2015)
Liu, W., Mei, T., Zhang, Y.: Instant mobile video search with layered audio-video indexing and progressive transmission. IEEE Trans. Multimed. 16(8), 2242–2255 (2014)
Chu, L., Zhang, Y., Li, G., Wang, S., Zhang, W., Huang, Q.: Effective multimodality fusion framework for cross-media topic detection. IEEE Trans. Circuits Syst. Video Technol. 26(3), 556–569 (2016)
Zeng, C., Ma, H.: Robust head-shoulder detection by PCA-based multilevel HOG-LBP detector for people counting. In: ICPR, pp. 2069–2072 (2010)
Chu, L., Jiang, S., Huang, Q.: Fast common visual pattern detection via radiate geometric model. In: ICIP, pp. 2465–2468 (2011)
Nie, W., Liu, A., Gao, Z., Su, Y.: Clique-graph matching by preserving global & local structure. In: CVPR, pp. 4503–4510 (2015)
Liu, A., Su, Y., Nie, W., Kankanhalli, M.: Hierarchical clustering multi-task learning for joint human action grouping and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 38(6), 1 (2016)
Gan, C., Wang, N., Yang, Y., Yeung, D.Y., Hauptmann, A.G.: DevNet: a deep event network for multimedia event detection and evidence recounting. In: CVPR, pp. 2568–2577 (2015)
Acknowledgment
This work is supported by the Funds for Creative Research Groups of China (No. 61421061), the Beijing Training Project for the Leading Talents in S&T (ljrc 201502), the National Natural Science Foundation of China (No. 61602049, 61402048), the CCF-Tencent Open Research Fund (No. AGR20160113).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Li, S., Liu, W., Ma, H., Fu, H. (2017). Multi-attribute Based Fire Detection in Diverse Surveillance Videos. In: Amsaleg, L., Guðmundsson, G., Gurrin, C., Jónsson, B., Satoh, S. (eds) MultiMedia Modeling. MMM 2017. Lecture Notes in Computer Science(), vol 10132. Springer, Cham. https://doi.org/10.1007/978-3-319-51811-4_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-51811-4_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-51810-7
Online ISBN: 978-3-319-51811-4
eBook Packages: Computer ScienceComputer Science (R0)