[4] [4] BARMPOUTIS P, DIMITROPOULOS K, GRAMMALIDIS N. Smoke detection using spatio-temporal analysis, motion modeling and dynamic texture recognition ［C］//Proceedings of the 2014 22nd European Signal Processing Conference. Lisbon: IEEE, 2014: 1078-1082.

[5] [5] PARK J O, KO B C, NAM J Y, et al. Wildfire smoke detection using spatiotemporal bag-of-features of smoke ［C］//Proceedings of 2013 IEEE Workshop on Applications of Computer Vision. Clearwater Beach: IEEE, 2013: 200-205.

[7] [7] ZHAO Y Q, LI Q J, GU Z. Early smoke detection of forest fire video using CS Adaboost algorithm ［J］. Optik-International Journal for Light and Electron Optics, 2015, 126(19): 2121-2124.

[9] [9] KIM Y J, KIM E G. Image based fire detection using convolutional neural network ［J］. Journal of the Korea Institute of Information and Communication Engineering, 2016, 20(9): 1649-1656.

[14] [14] TRAN D, WANG H, TORRESANI L, et al. A closer look at spatiotemporal convolutions for action recognition ［C］//Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 6450-6459.

[15] [15] IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift ［C］//Proceedings of the 32nd International Conference on Machine Learning. Lille: JMLR.org, 2015: 448-456.

[16] [16] GLOROT X, BORDES A, BENGIO Y. Deep sparse rectifier neural networks ［J］. Journal of Machine Learning Research, 2011, 15: 315-323.

[17] [17] HU J, SHEN L, ALBANIE S, et al. Squeeze-and-excitation networks ［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 42(8): 2011-2023.

[19] [19] YUAN F N. Video-based smoke detection with histogram sequence of LBP and LBPV pyramids ［J］. Fire Safety Journal, 2011, 46(3): 132-139.