Fire detection based on vision sensor and support vector machines

Ko, Byoung Chul; Cheong, Kwang-Ho; Nam, Jae-Yeal

doi:10.1016/j.firesaf.2008.07.006

Cited by 317 publications

(166 citation statements)

References 10 publications

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“…Interesting approaches related to fire motion analysis on video are not applicable for static images (Chunyu et al, 2010), and most of these approaches where found not to work with satisfactory performance (Celik et al, 2007;Ko et al, 2009;Liu and Ahuja, 2004).…”

Section: Related Workmentioning

confidence: 99%

Techniques for Effective and Efficient Fire Detection from Social Media Images

Bedo

Blanco

Oliveira

et al. 2015

Proceedings of the 17th International Conference on Enterprise Information Systems

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Abstract:Social media could provide valuable information to support decision making in crisis management, such as in accidents, explosions and fires. However, much of the data from social media are images, which are uploaded in a rate that makes it impossible for human beings to analyze them. Despite the many works on image analysis, there are no fire detection studies on social media. To fill this gap, we propose the use and evaluation of a broad set of content-based image retrieval and classification techniques for fire detection. Our main contributions are: (i) the development of the Fast-Fire Detection method (FFireDt), which combines feature extractor and evaluation functions to support instance-based learning; (ii) the construction of an annotated set of images with ground-truth depicting fire occurrences -the Flickr-Fire dataset; and (iii) the evaluation of 36 efficient image descriptors for fire detection. Using real data from Flickr, our results showed that FFireDt was able to achieve a precision for fire detection comparable to that of human annotators. Therefore, our work shall provide a solid basis for further developments on monitoring images from social media.

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Section: Related Workmentioning

confidence: 99%

Techniques for Effective and Efficient Fire Detection from Social Media Images

Bedo

Blanco

Oliveira

et al. 2015

Proceedings of the 17th International Conference on Enterprise Information Systems

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show abstract

“…In previous studies, [6][7][8][9][10] information from each pixel in the image was used to determine whether fires exist in the images. Alternatively, our paper proposes a method to model the local characteristics of fire, represented in sequential images based on patches of an input image, and to detect a fire area by using a supervised learning algorithm.…”

Section: Fire Detection Algorithmmentioning

confidence: 99%

“…Z. Teng 9 modeled the sequentially changing image pixel values by a hidden Markov model framework. Ko et al 10 applied a radial basis function kernel to two-class support vector machines. This research that uses machine learning algorithms could have an over-fitting problem and are not suitable for various environments.…”

Section: Introductionmentioning

confidence: 99%

Fire detection system using random forest classification for image sequences of complex background

Kim¹,

Kang²

2013

Opt. Eng

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Abstract. We present a fire alarm system based on image processing that detects fire accidents in various environments. To reduce false alarms that frequently appeared in earlier systems, we combined image features including color, motion, and blinking information. We specifically define the color conditions of fires in hue, saturation and value, and RGB color space. Fire features are represented as intensity variation, color mean and variance, motion, and image differences. Moreover, blinking fire features are modeled by using crossing patches. We propose an algorithm that classifies patches into fire or nonfire areas by using random forest supervised learning. We design an embedded surveillance device made with acrylonitrile butadiene styrene housing for stable fire detection in outdoor environments. The experimental results show that our algorithm works robustly in complex environments and is able to detect fires in real time.

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“…To lower the false alarm rate, potential fire regions can be discarded when they do not comply with an expected deformation model [3,6,[8][9][10]. Checking the dynamic characteristics of the potential fire's outline is also good practice for the reduction of false positives [11,12].…”

Section: Introductionmentioning

confidence: 99%

A Vision-Based Approach to Fire Detection

Gomes

Santana

Barata

2014

International Journal of Advanced Robotic Systems

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This paper presents a vision-based method for fire detection from fixed surveillance smart cameras. The method integrates several well-known techniques properly adapted to cope with the challenges related to the actual deployment of the vision system. Concretely, background subtraction is performed with a context-based learning mechanism so as to attain higher accuracy and robustness.The computational cost of a frequency analysis of potential fire regions is reduced by means of focusing its operation with an attentive mechanism. For fast discrimination between fire regions and fire-coloured moving objects, a new colour-based model of fire's appearance and a new wavelet-based model of fire's frequency signature are proposed. To reduce the false alarm rate due to the presence of fire-coloured moving objects, the category and behaviour of each moving object is taken into account in the decision-making. To estimate the expected object's size in the image plane and to generate geo-referenced alarms, the camera-world mapping is approximated with a GPS-based calibration process. Experimental results demonstrate the ability of the proposed method to detect fires with an average success rate of 93.1 % at a processing rate of 10 Hz, which is often sufficient for real-life applications.

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Fire detection based on vision sensor and support vector machines

Cited by 317 publications

References 10 publications

Techniques for Effective and Efficient Fire Detection from Social Media Images

Techniques for Effective and Efficient Fire Detection from Social Media Images

Fire detection system using random forest classification for image sequences of complex background

A Vision-Based Approach to Fire Detection

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