[1] [1] Lu Hongtao, Zhang Qinchuan. Overview of application of depth convolutional neural network in computer vision［J］. Data Acquisition and Processing, 2016, 31(1): 1-17.

[2] [2] Krizhevsky A, Sutskever I, Hinton G E. Image net classification with deep convolutional neural networks［C］. Advances in Neural Information Processing Systems(NIPS), 2012: 1097-1105.

[3] [3] Zhang Wei. Design and implementation of intelligent home system based on machine learning［D］. Jilin: Jilin University, 2016: 25-37.

[4] [4] Abadi M, Agarwal A, Barham P, et al. TensorFlow: large-scale machine learning on heterogeneous systems［J/OL］. ［2017-03-01］. https://arxiv.org/pdf/1603.04467.pdf.

[5] [5] Jia Y Q, Shelhamer E, Donahue J, et al. Caffe: convolutional architecture for fast feature embedding［C］. Proceedings of the 22nd ACM International Conference on Multimedia, 2014: 675-678.

[6] [6] Li Yandong, Hao Zongbo, Lei Hang. A survey of convolutional neural networks［J］. Journal of Computer Applications, 2016, 36(9): 2508-2515, 2565.

[7] [7] Sun Zhiyuan, Lu Chengxiang, Shi Zhongzhi, et al. Research and development of deep learning［J］. Computer Science, 2016, 43(2): 1-8.

[8] [8] Hu Zhengping, Chen Junling, Wang Meng. New development of classification model based on convolutional neural network in pattern recognition［J］. Journal of Yanshan University, 2015, 39(4): 283-291.

[9] [9] Hu Peng, Yang Weiqing, Fan Yong. Multi object tracking algorithm based on region convolution neural network［J］. Journal of Southwest University of Science and Technology, 2016, 31(1): 67-72.

[10] [10] Wang Zhen, Gao Maoting. Design and implementation of image recognition algorithm based on convolutional neural network［J］. Modern Computer, 2015(20): 61-66.

[11] [11] Rui Ting, Fei Jianchao, Zhou You. Pedestrian detection based on depth convolutional neural network［J］. Computer Engineering and Application, 2016, 52(13): 162-166.

[12] [12] Wang Haidong, Ma Xiaoshan, Yang Zhen, et al. Artificial neural network for atmospheric point spread function［J］. Laser & Optoelectronics Progress, 2016, 53(10): 102801.

[13] [13] Wang Hongqiao, Cai Yanning, Fu Guangyuan, et al. Image recognition and tracking based on image sequences for slow moving targets in the ground［J］. Laser & Optoelectronics Progress, 2016, 53(5): 051501.

[14] [14] He Pengcheng. An improved convolution neural network model and its application［D］. Dalian: Dalian University of Technology, 2016.

[15] [15] Bengio Y, Courville A, Vincent P. Representation learning: a review and new perspectives［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1798-1828.

[16] [16] Zhang Jun, Li Xin. Handwritten character recognition based on TensorFlow platform［J］. Computer Knowledge and Technology, 2016, 12(16): 199-201.

[17] [17] Yang Xiangnan. Research on target tracking algorithm based on convolution neural network and nested network［D］. Xiamen: Huaqiao University, 2016.

[18] [18] Zhang Zifu. Research and implementation of object recognition and tracking algorithm based on convolutional neural network［D］. Jilin: Jilin University, 2015.

[19] [19] Mao Ning, Yang Dedong, Yang Fucai, et al. Adaptive target tracking based on hierarchical convolution［J］. Laser & Optoelectronics Progress, 2016, 53(12): 051502.

[20] [20] Yilmaz A, Javed O, Shah M. Object tracking: a survey［J］. ACM Computing Surveys, 2006, 38(4): 1-45.

[21] [21] Hare S, Golodetz S, Saffari A, et al. Struck: structured output tracking with kernels［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(10): 2096-2109.

[22] [22] Kalal Z, Matas J, Mikolajczyk K. P-N learning: bootstrapping binary classifiers by structural constraints［C］. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010: 49-56.

[23] [23] Kalal Z, Mikolajczyk K, Matas J. Tracking-learning-detection［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1409-1422.