Real-time smoke and flame detection in video

Töreyi̇n, Behçet Uğur; Dedeoğlu, Yiğithan; Çetin, A. Enis

doi:10.1109/siu.2005.1567780

Cited by 79 publications

(116 citation statements)

References 6 publications

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“…But the algorithm is timeconsuming, and it is difficult to meet the real-time requirements of fire smoke detection. Toreyin et al (2005) uses the background subtraction and wavelet transform methods to extract effective features of edge, texture and motion of smoke. Chen et al (2006) shows that the color of a smoke pixel is in general gray.…”

Section: Motivations and Related Workmentioning

confidence: 99%

Video Fire Smoke Detection Based on Static Features of Smoke

Zhou¹,

Song²,

Yu³

2015

JAC

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Fire accidents have become one of the major threats to the properties and safety of human beings. Since conventional detection methods often fail to detect fire accidents in their early stages, detection technologies based on video images have become an important alternative for early detection of fire accidents. In this paper, we develop a new method that can accurately detect smoke that often appears in early stages of a fire accident. The method processes video images and identifies regions that may contain moving targets with a background subtraction approach using a Gaussian Mixture Model. A number of static features associated with these regions are then extracted and analyzed based on the corresponding features of smoke regions, which can recognize and eliminate regions that are similar to smoke regions but do not contain smoke. Our testing results show that the impacts from weather and other external environments can then be effectively reduced with this new approach and fire smoke can be accurately and quickly detected with a low false alarm rate.

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Section: Motivations and Related Workmentioning

confidence: 99%

Video Fire Smoke Detection Based on Static Features of Smoke

Zhou¹,

Song²,

Yu³

2015

JAC

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“…The pixel intensity at a certain location of all frames forms a time series. Frequency based method, such as wavelet or Fourier transform, can be used to identify smoke in videos through processing the pixel intensity time series [8]. Without any frame alignment, Fourier transforms are performed on the frames of original video clips "tank" and "pipe" respectively.…”

Section: Actual Video Datamentioning

confidence: 99%

Automatic alignment of infrared video frames for equipment leak detection

Zhou¹,

Zeng²

2007

Analytica Chimica Acta

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Petroleum refining and petrochemical industries start using infrared (IR) cameras to detect volatile organic compounds (VOC) leaking out of process equipment. However, further quantitative processing of these video image data or automatic recognition of VOC plumes are hindered by unaligned video frames owing to the slight vibrations of the camera. An automatic method is developed to align the IR video frames as a preprocessing procedure for other possible video processing methods. The alignment method is based on a two-dimensional spatial Fourier transform. The accuracy can reach fractional pixels in estimation of translational shift and 1-2 • for rotational shift. Temporal Fourier transform of actual industrial tests of IR videos is performed with both unaligned and aligned video frames. The results indicate that only after the alignment of the video frames, the camera motion interferences on VOC plume identification can be eliminated or minimized, and the VOC plume can be identified through investigating the characteristic flickering frequency power in the temporal Fourier transform. This alignment method provides a useful tool for IR or other optical video image data preprocessing purposes.

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“…In a previous study, Gomez-Rodriguez et al [3] applying wavelet and optical flow methods to detect smoke presence. Meanwhile, Toreyin et al in [4] introducing the wavelet coefficient method by carrying out temporal and spatial wavelet analysis. Research by Avgerinakis et al [2] implemented a smoke detection algorithm using temporal HOGHOF descriptors and energy color statistics.…”

Section: A Introductionmentioning

confidence: 99%

Early Smoke Detection on Video Using Wavelet Energy

Shafar

Wirayuda

Sthevanie

2017

IndoJC

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Most of the smoke detection system these days still using sensors that have to receive specific particles before it could give a warning. But, this system takes some time to react and quite difficult to place in spacious room or the outdoor. To overcome this, there is some research that build smoke detection system using many kind video processing technique that could provide early warning. In this research, wavelet energy was used to detect smoke in the video. To determine candidate blocks in a frame that contain smoke, this research performed background subtraction and color analysis based on HSV color space. Then implementing spatial analysis and spatio-temporal analysis by using wavelet energy method and accumulative motion orientation to detect the smoke. This system using combination of dataset from previous research [1], downloaded from various sources and selfmade dataset. Based on testing process using those dataset, this system reaches 91.05% accuracy for block-level and 72.22% accuracy for frame-level.Keywords: Accumulative motion orientation, smoke detection, spatial analysis, spatio-temporal analysis, video processing, wavelet energy Abstrak Sebagian besar sistem deteksi asap saat ini masih menggunakan sensor yang akan menangkap partikel tertentu sebelum dapat memberi peringatan. Namun, sistem ini membutuhkan waktu yang cukup lama untuk bereaksi dan sulit jika dipasang di ruang luas ataupun terbuka. Untuk mengatasi kekurangan ini, banyak dilakukan penelitian yang membangun sistem deteksi asap menggunakan berbagai teknik video processing yang dapat lebih menjamin untuk memberi peringatan dini. Dalam penelitian ini, digunakan metode wavelet energy untuk mendeteksi asap pada video. Untuk menentukan kandidat blok pada frame yang mengandung asap, dilakukan background subtraction dan analisa warna berdasarkan model HSV. Lalu digunakan metode wavelet energy pada spatial analysis dan spatio-temporal analysis serta metode accumulative motion orientation untuk mendeteksi asap. Dataset yang digunakan pada sistem yang dibangun merupakan kombinasi dari dataset penelitian sebelumnya [1], unduhan dari berbagai sumber serta dataet yang dibangun secara mandiri. Berdasarkan pengujian yang dilakukan menggunakan dataset tersebut, sistem ini menghasilkan tingkat akurasi sebesar pada level blok sebesar 91.05% dan tingkat akurasi pada level frame sebesar 72.22%.

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Real-time smoke and flame detection in video

Cited by 79 publications

References 6 publications

Video Fire Smoke Detection Based on Static Features of Smoke

Video Fire Smoke Detection Based on Static Features of Smoke

Automatic alignment of infrared video frames for equipment leak detection

Early Smoke Detection on Video Using Wavelet Energy

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