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Smoke detection in videos using non-redundant local binary pattern-based features

Conference Paper


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.

Authors


Publication Date


  • 2011

Citation


  • 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.

Scopus Eid


  • 2-s2.0-84055217986

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2194

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 4

Place Of Publication


  • USA

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.

Authors


Publication Date


  • 2011

Citation


  • 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.

Scopus Eid


  • 2-s2.0-84055217986

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2194

Has Global Citation Frequency


Start Page


  • 1

End Page


  • 4

Place Of Publication


  • USA