Improving Fire Detection Reliability by a Combination of Videoanalytics

Lascio, Rosario Di; Greco, Antonio; Vento, Mario

doi:10.1007/978-3-319-11758-4_52

Cited by 52 publications

(31 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The F1score graph with different velocity values of V min is in Figure 10. We have also compared our performance with Di Lascio et al [14] and Habibouglu et al [13] on the MIVIA dataset, results is on Table V. Our performance evaluation also includes a comparison of our method with Habibouglu et al on the FDDS dataset [13]. The quantitative results obtained by [13] and by our method are compared in Table IV.…”

Section: Resultsmentioning

confidence: 98%

“…We have also tested our method on the MIVIA dataset [14], [21], which contains 14 clips characterized by the presence of fire and 17 clips with no fire. Some clips contains objects which can be misclassified as fire, such as red boxes and street signs.…”

Section: Resultsmentioning

confidence: 99%

“…Although the method can be computationally efficient, the approach performs poorly when the fire area is small. Di Lascio et al [14] have deployed the rule-based color model of [8], which extracts fire color pixels on the YUV space. Motion is analyzed by comparing the consecutive frames using the Scale Invariant Feature Transform (SIFT).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fire Detection in Videos of Violent Crowds Acquired with Handheld Devices

Moria

Albu

2015

2015 12th Conference on Computer and Robot Vision

View full text Add to dashboard Cite

Our paper proposes a novel method for fire detection in riot videos acquired with handheld cameras and smart-phones. This is a typical example of computer vision in the wild, where we have no control over the data acquisition process, and the quality of the video data varies considerably. We propose a novel spatial model for fire, based on Gaussian mixtures and on color adjacency in the visible spectrum of incandescence. We demonstrate that using this spatial model in concert with motion cues leads to highly accurate results for fire detection in noisy, complex scenes of violent crowds.

show abstract

Section: Resultsmentioning

confidence: 98%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Fire Detection in Videos of Violent Crowds Acquired with Handheld Devices

Moria

Albu

2015

2015 12th Conference on Computer and Robot Vision

View full text Add to dashboard Cite

show abstract

“…Their algorithm can distinguish between rigid moving objects and a flame, based on a feature vector extracted from the optical flow and the physical behavior of a fire. De Lascio et al [24] combined color and motion information for the detection of fire in surveillance videos. Dimitropoulos et al [25] used spatio-temporal features based on texture analysis followed by an SVM classifier to classify candidate regions of the video frames into fire and non-fire.…”

Section: Introductionmentioning

confidence: 99%

Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications

Muhammad

Ahmad

et al. 2019

IEEE Trans. Syst. Man Cybern, Syst.

343

143

View full text Add to dashboard Cite

show abstract

“…Compared with traditional methods, VFR can recognize the flame itself directly through extracting the features of flame images, such as motion, edge blurring, color and spatial difference. Rosario et al [4] used YUV color space to select the suspected flame region. Then according to the diffusion characteristics of flame, the effective detection of flame region was achieved based on the displacement vector variation of SIFT feature points.…”

Section: Introductionmentioning

confidence: 99%

Combustion State Recognition of Flame Images Using Radial Chebyshev Moment Invariants Coupled with an IFA-WSVM Model

et al. 2018

View full text Add to dashboard Cite

Accurate combustion state recognition of flame images not only plays an important role in social security, but also contributes to increasing thermal efficiency and product quality. To improve the accuracy of feature extraction and achieve the combustion state recognition, a novel method based on radial Chebyshev moment invariants (RCMIs) and an improved firefly algorithm-wavelet support vector machine (IFA-WSVM) model is proposed. Firstly, the potential flame pixels and the potential flame contour are obtained in the pre-processing phase. Then, the rotation, translation and scaling (RTS) invariants of radial Chebyshev moments are derived. Combing the region and contour moments, the RCMIs of pre-processed and edge images are calculated to construct multi-feature vectors. To enhance the recognition performance, an IFA-WSVM model is built, where the IFA is applied to search the best parameters of WSVM. Then, the IFA-WSVM model is used to recognize the combustion state. Finally, the result for case studies show that the proposed method is superior to methods based on HMIs and ZMIs, achieving the highest rate of 99.07% in real time. The IFA algorithm also outperforms other benchmark algorithms. Even for the images transformed by RTS and small size of training sets, the proposed method continues to exhibit the best performance.

show abstract

Improving Fire Detection Reliability by a Combination of Videoanalytics

Cited by 52 publications

References 10 publications

Fire Detection in Videos of Violent Crowds Acquired with Handheld Devices

Fire Detection in Videos of Violent Crowds Acquired with Handheld Devices

Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications

Combustion State Recognition of Flame Images Using Radial Chebyshev Moment Invariants Coupled with an IFA-WSVM Model

Contact Info

Product

Resources

About