|| A. Mohan, K. Gauen, Y. H. Lu, W. W. Li, and X. Chen, "Internet of video things in 2030: A world with many cameras," in 2017 IEEE International Symposium on Circuits and Systems (ISCAS), 2017, pp. 1-4.|
 X. Wang, "Intelligent multi-camera video surveillance: A review," Pattern recognition letters, vol. 34, no. 1, pp. 3-19, 2013.
 A. Jain, L. Hong, and S. Pankanti, "Biometric identification," Communications of the ACM, vol. 43, no. 2, pp. 90-98, 2000.
 A. K. Jain, A. Ross, and S. Prabhakar, "An introduction to biometric recognition," IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 4-20, 2004.
 J. Galbally, S. Marcel, and J. Fierrez, "Image Quality Assessment for Fake Biometric Detection: Application to Iris, Fingerprint, and Face Recognition," IEEE Transactions on Image Processing, vol. 23, no. 2, pp. 710-724, 2014.
 Y. Peng, L. Spreeuwers, and R. Veldhuis, "Designing a Low-Resolution Face Recognition System for Long-Range Surveillance," in 2016 International Conference of the Biometrics Special Interest Group (BIOSIG), 2016, pp. 1-5.
 R. Amandi, M. Bayat, K. Minakhani, H. Mirloo, and M. Bazarghan, "Long distance iris recognition," in 2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP), 2013, pp. 164-168.
 S. Sarkar, P. J. Phillips, Z. Liu, I. R. Vega, P. Grother, and K. W. Bowyer, "The humanID gait challenge problem: data sets, performance, and analysis," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 2, pp. 162-177, 2005.
 J. Zhang, J. Pu, C. Chen, and R. Fleischer, "Low-Resolution Gait Recognition," IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 40, no. 4, pp. 986-996, 2010.
 內政部統計處. (2017). 106年第48週內政統計通報 [Online]. Available: https://www.moi.gov.tw/stat/news_detail.aspx?sn=13118
 Z. Wang, Z. Miao, Q. J. Wu, Y. Wan, and Z. Tang, "Low-resolution face recognition: a review," The Visual Computer, vol. 30, no. 4, pp. 359-386, 2014.
 W. Kim and C. Jung, "Illumination-invariant background subtraction: Comparative review, models, and prospects," IEEE Access, vol. 5, pp. 8369-8384, 2017.
 A. Sokolova and A. Konushin, "Gait recognition based on convolutional neural networks," The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 42, p. 207, 2017.
 Z. Wu, Y. Huang, L. Wang, X. Wang, and T. Tan, "A comprehensive study on cross-view gait based human identification with deep cnns," IEEE transactions on pattern analysis and machine intelligence, vol. 39, no. 2, pp. 209-226, 2017.
 Y. Guan and C.-T. Li, "A robust speed-invariant gait recognition system for walker and runner identification," in Biometrics (ICB), 2013 International Conference on, 2013, pp. 1-8: IEEE.
 O. Barnich and M. V. Droogenbroeck, "ViBe: A Universal Background Subtraction Algorithm for Video Sequences," IEEE Transactions on Image Processing, vol. 20, no. 6, pp. 1709-1724, 2011.
 K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis, "Real-time foreground-background segmentation using codebook model," Real-Time Imaging, vol. 11, no. 3, pp. 172-185, 2005.
 Z. Zivkovic, "Improved adaptive Gaussian mixture model for background subtraction," in Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004, vol. 2, pp. 28-31 Vol.2.
 C.-L. Hwang and H.-H. Huang, "Experimental validation of a car-like automated guided vehicle with trajectory tracking, obstacle avoidance, and target approach," in Industrial Electronics Society, IECON 2017-43rd Annual Conference of the IEEE, 2017, pp. 2858-2863: IEEE.
 J. Choi, D. Kim, H. Yoo, and K. Sohn, "Rear obstacle detection system based on depth from Kinect," in Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference on, 2012, pp. 98-101: IEEE.
 A. Sokolova and A. Konushin, "Pose-based Deep Gait Recognition," arXiv preprint arXiv:1710.06512, 2017.
 J. Hu, L. Shen, and G. Sun, "Squeeze-and-excitation networks," arXiv preprint arXiv:1709.01507, 2017.
 A. Karpathy, G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei, "Large-Scale Video Classification with Convolutional Neural Networks," in 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 1725-1732.
 M. Wang and W. Deng, "Deep Face Recognition: A Survey," arXiv preprint arXiv:1804.06655, 2018.
 G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, "Labeled faces in the wild: A database for studying face recognition in unconstrained environments," Technical Report 07-49, University of Massachusetts, Amherst2007.
 C. Wan, L. Wang, and V. V. Phoha, "A survey on gait recognition," ACM Computing Surveys (CSUR), vol. 51, no. 5, p. 89, 2018.
 M. Ju and B. Bir, "Individual recognition using gait energy image," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 2, pp. 316-322, 2006.
 L. H. Juang, S. A. Lin, and M. N. Wu, "Gender Recognition Studying by Gait Energy Image Classification," in 2012 International Symposium on Computer, Consumer and Control, 2012, pp. 837-840.
 X. Hongye and H. Zhuoya, "Gait recognition based on gait energy image and linear discriminant analysis," in 2015 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), 2015, pp. 1-4.
 X. Huang and N. V. Boulgouris, "Gait Recognition With Shifted Energy Image and Structural Feature Extraction," IEEE Transactions on Image Processing, vol. 21, no. 4, pp. 2256-2268, 2012.
 A. G. Binsaadoon and E. S. M. El-Alfy, "Kernel-Based Fuzzy Local Binary Pattern for Gait Recognition," in 2016 European Modelling Symposium (EMS), 2016, pp. 35-40.
 S. Sivapalan, D. Chen, S. Denman, S. Sridharan, and C. Fookes, "3D ellipsoid fitting for multi-view gait recognition," in 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2011, pp. 355-360.
 T. Krzeszowski, A. Michalczuk, B. Kwolek, A. Switonski, and H. Josinski, "Gait recognition based on marker-less 3D motion capture," in 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, 2013, pp. 232-237.
 C. Fengjiang, D. Muqing, and W. Cong, "Kinect-based gait recognition system design via deterministic learning," in 2017 29th Chinese Control And Decision Conference (CCDC), 2017, pp. 5916-5921.
 M. Li, J. Jiang, Y. Jia, and B. Lin, "A review of gait recognition based on vision," in 2016 5th International Conference on Computer Science and Network Technology (ICCSNT), 2016, pp. 823-827.
 C. C. Lee, C. H. Chuang, J. W. Hsieh, M. X. Wu, and K. C. Fan, "Frame difference history image for gait recognition," in 2011 International Conference on Machine Learning and Cybernetics, 2011, vol. 4, pp. 1785-1788.
 B. Ye and Y.-M. Wen, "Gait recognition based on DWT and SVM," in Wavelet Analysis and Pattern Recognition, 2007. ICWAPR′07. International Conference on, 2007, vol. 3, pp. 1382-1387: IEEE.
 F. M. Castro, M. J. Marin-Jimenez, N. Guil, S. Lopez-Tapia, and N. P. d. l. Blanca, "Evaluation of Cnn Architectures for Gait Recognition Based on Optical Flow Maps," in 2017 International Conference of the Biometrics Special Interest Group (BIOSIG), 2017, pp. 1-5.
 T. Wolf, M. Babaee, and G. Rigoll, "Multi-view gait recognition using 3D convolutional neural networks," in Image Processing (ICIP), 2016 IEEE International Conference on, 2016, pp. 4165-4169: IEEE.
 K. Shiraga, Y. Makihara, D. Muramatsu, T. Echigo, and Y. Yagi, "GEINet: View-invariant gait recognition using a convolutional neural network," in 2016 International Conference on Biometrics (ICB), 2016, pp. 1-8.
 A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet classification with deep convolutional neural networks," in Advances in neural information processing systems, 2012, pp. 1097-1105.
 I. J. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio, "Maxout networks," arXiv preprint arXiv:1302.4389, 2013.
 D.-A. Clevert, T. Unterthiner, and S. Hochreiter, "Fast and accurate deep network learning by exponential linear units (elus)," arXiv preprint arXiv:1511.07289, 2015.
 J. Duchi, E. Hazan, and Y. Singer, "Adaptive subgradient methods for online learning and stochastic optimization," Journal of Machine Learning Research, vol. 12, no. Jul, pp. 2121-2159, 2011.
 M. D. Zeiler, "ADADELTA: an adaptive learning rate method," arXiv preprint arXiv:1212.5701, 2012.
 D. P. Kingma and J. Ba, "Adam: A method for stochastic optimization," arXiv preprint arXiv:1412.6980, 2014.
 M. Lin, Q. Chen, and S. Yan, "Network in network," arXiv preprint arXiv:1312.4400, 2013.
 R. Alp Güler, N. Neverova, and I. Kokkinos, "Densepose: Dense human pose estimation in the wild," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 7297-7306.
 N. Takemura, Y. Makihara, D. Muramatsu, T. Echigo, and Y. Yagi, "Multi-view large population gait dataset and its performance evaluation for cross-view gait recognition," IPSJ Transactions on Computer Vision and Applications, vol. 10, no. 1, p. 4, 2018.
 H. Iwama, M. Okumura, Y. Makihara, and Y. Yagi, "The ou-isir gait database comprising the large population dataset and performance evaluation of gait recognition," IEEE Transactions on Information Forensics and Security, vol. 7, no. 5, pp. 1511-1521, 2012.
 C. A. o. S. Institute of Automation. (2001). CASIA Gait Database [Online]. Available: http://www.sinobiometrics.com
 Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh, "Realtime multi-person 2d pose estimation using part affinity fields," arXiv preprint arXiv:1611.08050, 2016.
 K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
 F. Schroff, D. Kalenichenko, and J. Philbin, "Facenet: A unified embedding for face recognition and clustering," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 815-823.
 W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song, "Sphereface: Deep hypersphere embedding for face recognition," in The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, vol. 1.
 H. Wang et al., "CosFace: Large margin cosine loss for deep face recognition," arXiv preprint arXiv:1801.09414, 2018.
 J. Deng, J. Guo, and S. Zafeiriou, "Arcface: Additive angular margin loss for deep face recognition," arXiv preprint arXiv:1801.07698, 2018.
 A. Hermans, L. Beyer, and B. Leibe, "In defense of the triplet loss for person re-identification," arXiv preprint arXiv:1703.07737, 2017.
 K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770-778.
 D. Sun, X. Yang, M.-Y. Liu, and J. Kautz, "Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 8934-8943.
 E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, and T. Brox, "Flownet 2.0: Evolution of optical flow estimation with deep networks," in IEEE conference on computer vision and pattern recognition (CVPR), 2017, vol. 2, p. 6.
 Y. Fu et al., "Horizontal Pyramid Matching for Person Re-identification," arXiv preprint arXiv:1804.05275, 2018.
 A. Ranjan, J. Romero, and M. J. Black, "Learning Human Optical Flow," arXiv preprint arXiv:1806.05666, 2018.