Application of panoramic annular lens for motion analysis tasks: surveillance and smoke detection

Kopilovic, I.; Vágvölgyi, Bence; Szirányi, Tamás

doi:10.1109/icpr.2000.903017

Cited by 45 publications

(29 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…illustrates when the R component increases toward the upmost value 255 and then saturation will decrease downward to zero [1,3]. We set S T and R T to 60 and 170, respectively on the basis of our experiments, and the application of the above three rules to our algorithm generated the results shown in Fig.…”

Section: Fire-color Pixels Detectionmentioning

confidence: 99%

See 1 more Smart Citation

Fire detection based on hidden Markov models

Zhu

Kim

Kang

2010

Int. J. Control Autom. Syst.

View full text Add to dashboard Cite

In this paper, a novel method of real-time fire detection based on HMMs is presented. First, we present an analysis of fire characteristics that provides evidence supporting the use of HMMs to detect fire; second, we propose an algorithm for detecting candidate fire pixels that entails the detection of moving pixels, fire-color inspection, and pixels clustering. The main contribution of this paper is the establishment and application of a hidden Markov fire model by combining the state transition between fire and non-fire with fire motion information to reduce data redundancy. The final decision is based on this model which includes training and application; the training provides parameters for the HMM application. The experimental results show that the method provides both a high detection rate and a low false alarm rate. Furthermore, real-time detection has been effectively realized via the learned parameters of the HMM, since the most time-consuming components such as HMM training are performed off-line.

show abstract

Section: Fire-color Pixels Detectionmentioning

confidence: 99%

“…In recent decades, video processing techniques for fire detection have been studied by many researchers [1][2][3][4][5][6][7]. Primarily, only color clues of the fire were used [2].…”

Section: Introductionmentioning

confidence: 99%

Fire detection based on hidden Markov models

Zhu

Kim

Kang

2010

Int. J. Control Autom. Syst.

View full text Add to dashboard Cite

show abstract

“…For example, Kopilovic et al (2000) propose to use the features of the irregular movements of smoke to recognize it. Specifically, a method based on optical flow is developed to analyze and judge the suspected smoke area.…”

Section: Motivations and Related Workmentioning

confidence: 99%

Video Fire Smoke Detection Based on Static Features of Smoke

Zhou¹,

Song²,

Yu³

2015

JAC

View full text Add to dashboard Cite

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.

show abstract

“…Others adopted panoramic camera, wavelet transform, neural networks, etc. for relatively complicated fire detection systems [6][7][8]. And some were very specific purposed systems like tunnel fire, ship compartmentfire, forest fire, etc [9][10][11].…”

Section: Introductionmentioning

confidence: 99%