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-  2016 

基于级联卷积神经网络的视频动态烟雾检测
Dynamic Smoke Detection Using Cascaded Convolutional Neural Network for Surveillance Videos

DOI: 10.3969/j.issn.1001-0548.2016.06.020

Keywords: 卷积神经网络,深度学习,纹理特征,视频烟雾检测

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Abstract:

复杂场景中烟雾特性的提取是目前视频烟雾检测领域的主要挑战。针对该问题,提出一种静态和动态特征结合的卷积神经网络视频烟雾检测框架。在静态单帧图像特征检测的基础上,进一步分析其时空域上的动态纹理信息以期克服复杂的环境干扰。实验结果显示,该级联卷积神经网络模型可有效应用于复杂视频场景中烟雾事件的实时检测。

References

[1]  YU Chun-yu, ZHANG Yong-ming, FANG Jun, et al. Texture analysis of smoke for real-time fire detection[C]//Second International Workshop on Computer Science and Engineering, WCSE'09.[S.l.]:IEEE, 2009, 2:511-515.
[2]  TIAN H, LI W, OGUNBONA P, et al. Smoke detection in videos using non-redundant local binary pattern-based features[C]//201113th IEEE International Workshop on Multimedia Signal Processing (MMSP).[S.l.]:IEEE, 2011:1-4.
[3]  LéCUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-2324.
[4]  KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Imagenet classification with deep convolutional neural networks[C]//Advances in Neural Information Processing Systems. Lake Tahoe, USA:[s.n.], 2012:1097-1105.
[5]  YIN Q, CAO Z, JIANG Y, et al. Learning deep face representation:U.S, Patent 20,150,347,820[P]. 2015-12-03.
[6]  ANNANE D, CHEVROLET J C, CHEVRET S, et al. Two-stream convolutional networks for action recognition in videos[J]. Advances in Neural Information Processing Systems, 2014, 1(4):568-576.
[7]  JIA Y, SHELHAMER E, DONAHUE J, et al. Caffe:Convolutional architecture for fast feature embedding[EB/OL]. (2014-06-20). http://arxiv.org/abs/1408.5093.
[8]  LI Fei-fei, FERGUS R, PERONA P. Learning generative visual models from few training examples:an incremental Bayesian approach tested on 101 object categories[C]//Computer Vision and Image Understanding.[S.l.]:Elsevier, 2004, 106(1):59-70.
[9]  SOOMRO K, ZAMIR R A, SHAH M. UCF101:a dataset of 101 human action classes from videos in the wild[EB/OL]. (2012-12-03). http://arxiv.org/abs/1212.0402.
[10]  OJALA T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2002, 24(7):971-987.
[11]  YUAN F. A fast accumulative motion orientation model based on integral image for video smoke detection[J]. Pattern Recognition Letters, 2008, 29(7):925-932.
[12]  YUAN F. A double mapping framework for extraction of shape-invariant features based on multi-scale partitions with AdaBoost for video smoke detection[J]. Pattern Recognition, 2012, 45(12):4326-4336.
[13]  TOREYIN B U, DEDEOGLU Y. Contour based smoke detection in video using wavelets[C]//14th European Signal Processing Conference.[S.l.]:IEEE, 2006:1-5.
[14]  TIAN H, LI W, WANG L, et al. Smoke detection in video:an image separation approach[J]. International journal of computer vision, 2014, 106(2):192-209.
[15]  YUAN F. Video-based smoke detection with histogram sequence of LBP and LBPV pyramids[J]. Fire Safety Journal, 2011, 46(3):132-139.
[16]  SZEGEDY C, LIU W, JIA Y, et al. Going deeper with convolutions[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).[S.l.]:IEEE Computer Society, 2014:1-9.
[17]  YU Chun-yu, FANG Jun, WANG Jin-jun, et al. Video fire smoke detection using motion and color features[J]. Fire Technology, 2010, 46(3):651-663.
[18]  HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[EB/OL]. (2015-12-10). http://arxiv.org/abs/1512.03385.
[19]  ZHAO G, PIETIK?INEN M. Dynamic texture recognition using local binary patterns with an application to facial expressions[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2007, 29(6):915-928.

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