NDLI: Smoke detection in videos using Non-Redundant Local Binary Pattern-based features

Content Provider	IEEE Xplore Digital Library
Author	Hongda Tian Wanqing Li Ogunbona, P. Duc Thanh Nguyen Ce Zhan
Copyright Year	2011
Description	Author affiliation: Advanced Multimedia Research Lab, ICT Research Institute, School of Computer Science and Software Engineering, University of Wollongong, Australia (Hongda Tian; Wanqing Li; Ogunbona, P.; Duc Thanh Nguyen; Ce Zhan)
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.
Starting Page	1
Ending Page	4
File Size	664093
Page Count	4
File Format	PDF
ISBN	9781457714320
e-ISBN	9781457714344
e-ISBN	9781457714337
DOI	10.1109/MMSP.2011.6093844
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-10-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Videos Vectors Histograms Real time systems Robustness Lighting
Content Type	Text
Resource Type	Article

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