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收稿：2019-05-22，
修回：2019-7-9，
录用：2019-7-16，
纸质出版：2019-10-16
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目的
2
检测烟雾可以预警火灾。视频监控烟雾比传统的单点探测器监控范围更广、反应更灵敏，对环境和安装的要求也更低。但是目前的烟雾检测算法，无论是利用烟雾的色彩、纹理等静态特征和飘动、形状变化或者频域变化等动态特征的传统方法，还是采用卷积神经网络、循环神经网络等深度学习的方法，准确率和敏感性都不高。
方法
2
本文着眼于烟雾的升腾特性，根据烟雾运动轨迹的右倾直线特性、连续流线型特性、低频特性、烟源固定特性和比例特性，采用切片的方式用卷积神经网络（CNN）抽取时间压缩轨迹的动态特征，用循环神经网络（RNN）抽取长程的时间关联关系，采用分块的方式提高空间分辨能力，能准确、迅速地识别烟雾轨迹并发出火灾预警。
结果
2
对比CNN、C3D（3d convolutional networks）、traj+SVM（trajectory by support vector machine）、traj+RNNs（trajectory by recurrent neural network）和本文方法traj+CNN+RNNs（trajectory by convolutional neural networks and recurrent neural network）以验证效果。CNN和C3D先卷积抽取特征，后分类。traj+SVM采用SVM辨识视频时间压缩图像中的烟雾轨迹，traj+RNNs采用RNNs分辨烟雾轨迹，traj+CNN+RNNs结合CNN和RNNs识别轨迹。实验表明，与traj+SVM相比，traj+CNN+RNNs准确率提高了35.2%，真负率提高15.6%。但是深度学习的方法往往计算消耗很大，traj+CNN+RNNs占用内存2.31 GB，网络权重261 MB，前向分析时帧率49帧/s，而traj+SVM帧率为178帧/s。但与CNN、C3D相比，本文方法较轻较快。为了进一步验证方法的有效性，采用一般方法难以识别的数据进一步测试对比这5个方法。实验结果表明，基于轨迹的方法仍然取得较好的效果，traj+CNN+RNNs的准确率、真正率、真负率和帧率还能达到0.853、0.847、0.872和52帧/s，但是CNN、C3D的准确率下降到0.585、0.716。
结论
2
从视频的时间压缩轨迹可以辨认出烟雾的轨迹，即便是早期的弱小烟雾也能准确识别，因此traj+CNN+RNNs辨识轨迹的方法有助于预警早期火灾。本文方法能够在较少的资源耗费下大幅度提高烟雾检测的准确性和敏感性。
Objective
2
Smoke detection by surveillance cameras is reasonable to warn fire. This technology has many advantages compared with other traditional point detectors. Wide areas could be covered
rapid respondence could be available
and installation and maintenance requirements could be less. However
the current smoke detection algorithms are unsatisfying in terms of accuracy and sensitivity due to the varying colors
shapes
and textures of smoke. The traditional studies focus on designing handcrafted features that extract such static features as colors
shapes
and textures and dynamic ones
including shape deforming
drifting
and frequency shifting. This task is time consuming. Although the algorithm exhibits good characteristics
maintaining its robustness for all environments is difficult. The detection effectiveness often sharply descends when these methods are applied in different environments. The fashionable methods
such as convolution neural network (CNN)
recurrent neural network (RNN)
and other statistical methods
are based on deep learning. However
applying these methods is difficult because the surveillance platforms have limited resources. These networks are also unsatisfying in terms of accuracy and sensitivity.
Method
2
The proposed method utilizes trajectories in condensed images
which are summed in horizontal and vertical directions for all video pixels. Smoke trajectories in condensed images are always right-leaning
straightly linear
proportional
and streamline-like with low frequencies and fixed starting points. Accordingly
surveillance videos are summed into condensed images
sliced
and then fed into CNN to extract features to find the long-term relationship by RNN. Partitioning strategy is also adopted to improve sensitivity. Therefore
the method uses not only the trajectory shapes but also the short- and long-range relationships in the time domain to detect the existence of smoke in videos.
Result
2
Controlled experiments of CNN
C3D(3d convolutional networks)
traj + SVM(support vector machine)
traj + RNNs
and traj + CNN + RNNs are conducted. The CNN and C3D methods are typical deep learning networks that initially extract features and then make judgments. The traj + SVM method detects smoke trajectories by traditional SVM algorithm
the traj + RNNs method finds smoke trajectories by RNNs
and the traj + CNN + RNNs method recognizes smoke trajectories by combining CNN and RNNs
which is the proposed method. The accuracy of the traj + CNN + RNNs method is increased by 35.2% compared with that of traj + SVM
and the real negative rate is increased by 15.6%. However
the computing cost of the traj + CNN + RNNs method is relatively high. The frame rate
maximum memory consumption
and network weight are 49 frame/s
2.31 GB
and 261 MB
respectively. By contrast
the frame rate of traj + SVM is 178 frame/s. The computing cost of deep learning networks is generally high. Nevertheless
the traj + CNN + RNNs method is the lightest and fastest among all deep learning networks. Some confusing data for many traditional methods are collected for the second experiment to further compare these methods. The methods based on trajectories
namely
traj + SVM
traj + RNNs
and traj + CNN + RNNs
remain at a good level
and the indexes of ACC(accaracy)
TPR(trure positive rate)
and TNR(true negative rate) and the sensitivity are 0.853
0.847
0.872
and 52/26(frame/s)
respectively. However
the corresponding indexes of CNN and C3D considerably reduced. The accuracies of CNN and C3D are 0.585 and 0.716
respectively.
Conclusion
2
The proposed method helps improve the accuracy and sensitivity of smoke detection. The smoke trajectories can be identified from the condensed images
even from those of early smoke
which are helpful for early fire warning.
Shi J T, Yuan F N, Xia X. Video smoke detection:a literature survey[J]. Journal of Image and Graphics, 2018, 23(3):303-322.
史劲亭, 袁非牛, 夏雪.视频烟雾检测研究进展[J].中国图象图形学报, 2018, 23(3):303-322.[DOI:10.11834/jig.170439]
Hohberg S P. Wildfire smoke detection using convolutional neural networks[D]. Berlin, Germany: Freie Universität Berlin, 2015.
Frizzi S, Kaabi R, Bouchouicha M, et al. Convolutional neural network for video fire and smoke detection[C]//Proceedings of the 42nd Annual Conference of the IEEE Industrial Electronics Society. Florence, Italy: IEEE, 2016: 877-882.[ DOI: 10.1109/IECON.2016.7793196 http://dx.doi.org/10.1109/IECON.2016.7793196 ]
Tao C Y, Zhang J, Wang P. Smoke detection based on deep convolutional neural networks[C] //Proceedings of the International Conference on Industrial Informatics-Computing Technology, Intelligent Technology, Industrial Information Integration. Wuhan, China: IEEE, 2016: 150-153.[ DOI: 10.1109/ICⅡCⅡ.2016.0045 http://dx.doi.org/10.1109/ICⅡCⅡ.2016.0045 ]
Chen J Z, Wang Z J, Chen H H, et al. Dynamic smoke detection using cascaded convolutional neural network for surveillance videos[J]. Journal of University of Electronic Science and Technology of China, 2016, 46(6):992-996.
陈俊周, 汪子杰, 陈洪瀚, 等.基于级联卷积神经网络的视频动态烟雾检测[J].电子科技大学学报, 2016, 46(6):992-996.[DOI:10.3969/j.issn.1001-0548.2016.06.020]
Filonenko A, Kurnianggoro L, Jo K H. Comparative study of modern convolutional neural networks for smoke detection on image data[C]//Proceedings of the 10th International Conference on Human System Interactions. Ulsan, South Korea: IEEE, 2017: 64-68.[ DOI: 10.1109/HSI.2017.8004998 http://dx.doi.org/10.1109/HSI.2017.8004998 ]
Yin M X, Lang C Y, Li Z, et al. Recurrent convolutional network for video-based smoke detection[J]. Multimedia Tools and Applications, 2019, 78(1):237-256.[DOI:10.1007/s11042-017-5561-5]
Yin Z J, Wan B Y, Yuan F N, et al. A deep normalization and convolutional neural network for image smoke detection[J]. IEEE Access, 2017, 5:18429-18438.[DOI:10.1109/ACCESS.2017.2747399]
Zhang Q X, Lin G H, Zhang Y M, et al. Wildland forest fire smoke detection based on faster r-cnn using synthetic smoke images[J]. Procedia Engineering, 2018, 211:441-446.[DOI:10.1016/j.proeng.2017.12.034]
Luo Y M, Zhao L, Liu P Z, et al. Fire smoke detection algorithm based on motion characteristic and convolutional neural networks[J]. Multimedia Tools and Applications, 2018, 77(12):15075-15092.[DOI:10.1007/s11042-017-5090-2]
Dung N M, Kim D, Ro S. A video smoke detection algorithm based on cascade classification and deep learning[J]. KSⅡ Transactions on Internet and Information Systems, 2018, 12(12):6018-6033.[DOI:10.3837/tiis.2018.12.022]
Li X Q, Chen Z X, Wu Q M J, et al. 3D parallel fully convolutional networks for real-time video wildfire smoke detection[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2018, (12):1-15.[DOI:10.1109/TCSVT.2018.2889193]
Yuan F N, Zhang L, Wan B Y, et al. Convolutional neural networks based on multi-scale additive merging layers for visual smoke recognition[J]. Machine Vision and Applications, 2019, 30(2):345-358.[DOI:10.1007/s00138-018-0990-3]
Luo S, Yan C W, Wu K L, et al. Smoke detection based on condensed image[J]. Fire Safety Journal, 2015, 75:23-35.
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL].[2019-05-21] . https://arxiv.org/pdf/1409.1556.pdf https://arxiv.org/pdf/1409.1556.pdf .
Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural Computation, 1997, 9(8):1735-1780.[DOI:10.1162/neco.1997.9.8.1735]
Tran D, Bourdev L, Fergus R, et al. Learning spatiotemporal features with 3d convolutional networks[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision. Santiago, Chile: IEEE, 2015: 4489-4497.[ DOI: 10.1109/ICCV.2015.510 http://dx.doi.org/10.1109/ICCV.2015.510 ]
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