1. 毕业设计（论文）的内容和要求
1、毕业设计的内容:随着科技的发展和进步，原来需要大量人力完成的重复性枯燥工作逐渐被交由计算机完成。
计算机视觉作为一门基于图像处理、机器学习和模式识别的交叉学科，是近年来快速发展的一个研究领域。
而智能视频监控技术应运而生并迅速成为一个研究热点，其主要任务是模拟人的视觉能力，试图建立能够从图像或者多维数据中获取信息的人工智能系统。
2. 参考文献
[1] M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen,C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al. Tensorflow: Large-scale machine learning on heterogeneous systems, 2015. Software available from tensorflow. org, 1,2015. 4[2] W. Chen, J. T. Wilson, S. Tyree, K. Q. Weinberger, and Y. Chen. Compressing neural networks with the hashing trick. CoRR, abs/1504.04788, 2015. 2[3] P. KaewTraKulPong and R. Bowden. An improved adaptive background mixture model for real-time tracking with shadow detection. in European Workshop on Advanced Video Based Surveillance Systems,(London, UK), September 2001.[4] Benenson, Rodrigo, et al. Ten Years of Pedestrian Detection, What Have We Learned? ECCV 2014 Workshops. Springer International Publishing, 2014:613-627[5] 李航. 统计学习方法[M]. 北京：清华大学出版社, 2012[6] J. Ba, V. Mnih, and K. Kavukcuoglu. Multiple object recognition with visual attention. ICLR, 2015.[7] J. Hays and A. Efros. Large-Scale Image Geolocalization.In J. Choi and G. Friedland, editors, Multimodal Location Estimation of Videos and Images. Springer, 2014. 6, 7[8] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385,2015. 1[9] G. Hinton, O. Vinyals, and J. Dean. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531, 2015.2, 7[10] J. Huang, V. Rathod, C. Sun, M. Zhu, A. Korattikara,A. Fathi, I. Fischer, Z. Wojna, Y. Song, S. Guadarrama, et al.Speed/accuracy trade-offs for modern convolutional object detectors. arXiv preprint arXiv:1611.10012, 2016. 7[11] I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio. Quantized neural networks: Training neural networks with low precision weights and activations. arXiv preprint arXiv:1609.07061, 2016. 2[12] F. N. Iandola, M. W. Moskewicz, K. Ashraf, S. Han, W. J.Dally, and K. Keutzer. Squeezenet: Alexnet-level accuracy with 50x fewer parameters and 1mb model size. arXiv preprint arXiv:1602.07360, 2016. 1, 6[13] S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167, 2015. 1, 3, 7[14] M. Jaderberg, A. Vedaldi, and A. Zisserman. Speeding up convolutional neural networks with low rank expansions. arXiv preprint arXiv:1405.3866, 2014. 2
