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Presentation
Smoke detection in videos using non-redundant local binary pattern-based features
Faculty of Informatics - Papers (Archive)
  • Hongda Tian, University of Wollongong
  • Wanqing Li, University of Wollongong
  • Philip Ogunbona, University of Wollongong
  • Duc Thanh Nguyen, University of Wollongong
  • Ce Zhan, University of Wollongong
RIS ID
50811
Publication Date
1-1-2011
Publication Details

Tian, H., Li, W., Ogunbona, P., Nguyen, D. & Zhan, C. (2011). Smoke detection in videos using non-redundant local binary pattern-based features. 13rd IEEE International Workshop on Multimedia Signal Processing, MMSP 2011 (pp. 1-4). USA: IEEE.

Abstract

This paper presents a novel and low complexity method for real-time video-based smoke detection. As a local texture operator, Non-Redundant Local Binary Pattern (NRLBP) is more discriminative and robust to illumination changes in comparison with original Local Binary Pattern (LBP), thus is employed to encode the appearance information of smoke. Non-Redundant Local Motion Binary Pattern (NRLMBP), which is computed on the difference image of consecutive frames, is introduced to capture the motion information of smoke. Experimental results show that NRLBP outperforms the original LBP in the smoke detection task. Furthermore, the combination of NRLBP and NRLMBP, which can be considered as a spatial-temporal descriptor of smoke, can lead to remarkable improvement on detection performance.

Citation Information
Hongda Tian, Wanqing Li, Philip Ogunbona, Duc Thanh Nguyen, et al.. "Smoke detection in videos using non-redundant local binary pattern-based features" (2011) p. 1 - 4
Available at: http://works.bepress.com/wli/29/