Forest Fire Smoke Video Detection Using Spatiotemporal and Dynamic Texture Features

Zhao, Yaqin; Zhou, Zhong; Xu, Mingming

doi:10.1155/2015/706187

Cited by 22 publications

(9 citation statements)

References 8 publications

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“…This number represents the texture information of the corresponding local image block. The related smoke detection references include [23], [24], [34], [36], [40], [48]- [51] • Others: Reference [37] develops a high order linear dynamical system to extract texture features from video frames. Reference [17] applies center symmetric local ternary patterns (CS-LTP) to generate texture features.…”

Section: C: Texture Featurementioning

confidence: 99%

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Optimal Placement and Intelligent Smoke Detection Algorithm for Wildfire-Monitoring Cameras

et al. 2020

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Smoke produced by wildfires is usually visible much earlier than flames. Hence, early detection of wildfire smoke is essential to prevent severe property losses and heavy casualties from catastrophic wildfires. Camera networks are being built and expanded to achieve timely wildfire smoke detection. To achieve the best camera coverage and detection accuracy with limited budget, an intelligent video smoke detection algorithm and an optimal wildfire camera placement strategy are in a critical need. In this paper, we propose an efficient video smoke detection framework designed for embedded applications on local cameras. It consists of two modules. In the first module, the original video frames are processed by local binary patterns and a dense optical flow estimator. In the second module, the produced features are then fed into a lightweight deep convolutional neural network, which serves as a binary classifier to detect the presence of smoke. We also formulate the wildfire camera placement problem as a binary integer programming problem to minimize the overall fire risk of a given area. Case studies on real-world videos are carried out to validate the accuracy as well as the computational and memory efficiency of the proposed smoke detection framework. We also validate our proposed camera placement strategy by simulating the deployment of wildfire cameras across a test region in Southern California.INDEX TERMS Smoke detection, camera placement, deep neural network, local binary patterns, optical flow.

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Section: C: Texture Featurementioning

confidence: 99%

“…The final decision is a weighted combination of all weak classifiers. The related video smoke detection references include [24], [36], [53].…”

Section: ) Classifiers Applied In Smoke Detectionmentioning

confidence: 99%

Optimal Placement and Intelligent Smoke Detection Algorithm for Wildfire-Monitoring Cameras

et al. 2020

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“…Jia et al [9] employed a salient smoke detection model based on colour and motion features to detect smoke. Zhao et al [10] extracted the spatiotemporal features and the dynamic texture features to present a novel method for forest fire detection. Local binary motion pattern (LBMP) is used to extract the dynamic texture features from videos.…”

Section: Related Workmentioning

confidence: 99%

Fusing texture, edge and line features for smoke recognition

Yuan

Xia

et al. 2019

IET image process

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To improve recognition accuracy, the authors fuse texture, edge and line information to propose a feature extraction method for smoke recognition. The Canny operator is proposed to generate an edge image from an original image, and then adopt the Hough transform to extract straight lines from the edge image. The lines are rasterised to generate a discrete line image and two local patterns are proposed for the edge and line images. The first one is local boundary summation pattern (LBSP) that computes the sum of binary pixel values along the boundary of a local region around a centre pixel. The second one is called local region summation pattern (LRSP) that sums up the binary values of pixels in a local region around the centre pixel. Besides LBSP and LRSP, LBPs with three mapping modes (LBP_M3) to achieve traditional texture information are also extracted. Finally, the authors concatenate the histograms of LBP_M3, LBSP and LRSP to generate a feature vector, and use support vector machine for classifying and testing. Experiments show that authors’ method outperforms most of existing traditional methods for smoke recognition. Although this method has low dimensional features, it also obtains good performance for multi‐class texture classification.

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“…Efforts to detect forest fire early has been made by using a machine learning approach, as an example, the one done by Zhao et al (2011). Almost all real-time detection approaches are using remote sensing technology in the early determination forest fire area (Alkhatib, 2014;Zhao et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

The Identification of Determinant Parameter in Forest Fire Based on Feature Selection Algorithms

Fitrianah

Fahmi

2019

Sinergi

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This research conducts studies of the use of the Sequential Forward Floating Selection (SFFS) Algorithm and Sequential Backward Floating Selection (SBFS) Algorithm as the feature selection algorithms in the Forest Fire case study. With the supporting data that become the features of the forest fire case, we obtained information regarding the kinds of features that are very significant and influential in the event of a forest fire. Data used are weather data and land coverage of each area where the forest fire occurs. Based on the existing data, ten features were included in selecting the features using both feature selection methods. The result of the Sequential Forward Floating Selection method shows that earth surface temperature is the most significant and influential feature in regards to forest fire, while, based on the result of the Sequential Backward Feature Selection method, cloud coverage, is the most significant. Referring to the results from a total of 100 tests, the average accuracy of the Sequential Forward Floating Selection method is 96.23%. It surpassed the 82.41% average accuracy percentage of the Sequential Backward Floating Selection method.

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Forest Fire Smoke Video Detection Using Spatiotemporal and Dynamic Texture Features

Cited by 22 publications

References 8 publications

Optimal Placement and Intelligent Smoke Detection Algorithm for Wildfire-Monitoring Cameras

Optimal Placement and Intelligent Smoke Detection Algorithm for Wildfire-Monitoring Cameras

Fusing texture, edge and line features for smoke recognition

The Identification of Determinant Parameter in Forest Fire Based on Feature Selection Algorithms

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