[1] Muro-de-la-Herran A, Garcia-Zapirain B, Mendez-Zorrilla A. Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications[J]. Sensors, 14, 3362-3394(2014). http://www.europepmc.org/abstract/MED/24556672

[2] Giroux M, Moissenet F, Biomedical Engineering. 16 sup1:, 152-154(2013).

[3] Rose J, Gamble J G. Human walking[M]. 2nd ed. Baltimore: Williams & Wilkins(1994).

[4] Yang C, Ugbolue U, Carse B et al. Multiple marker tracking in a single-camera system for gait analysis. [C]∥2013 IEEE International Conference on Image Processing, September 15-18, 2013, Melbourne, VIC, Australia. New York: IEEE, 3128-3131(2013).

[5] Huang B F, Chen M, Shi X et al. Gait event detection with intelligent shoes. [C]∥2007 International Conference on Information Acquisition, July 8-11, 2007, Seogwipo-si, Korea. New York: IEEE, 579-584(2007).

[6] Catalfamo P, Moser D, Ghoussayni S et al. Detection of gait events using an F-Scan in-shoe pressure measurement system[J]. Gait & Posture, 28, 420-426(2008). http://www.sciencedirect.com/science/article/pii/S096663620800043X

[7] Heliot R, Pissard-Gibollet R, Espiau B et al. Continuous identification of gait phase for robotics and rehabilitation using microsensors. [C]∥ICAR '05. Proceedings., 12th International Conference on Advanced Robotics, 2005, July 18-20, 2005, Seattle, WA, USA. New York: IEEE, 686-691(2005).

[8] Williamson R, Andrews B J. Gait event detection for FES using accelerometers and supervised machine learning[J]. IEEE Transactions on Rehabilitation Engineering, 8, 312-319(2000). http://europepmc.org/abstract/MED/11001511

[9] Hanlon M, Anderson R. Real-time gait event detection using wearable sensors[J]. Gait & Posture, 30, 523-527(2009). http://www.sciencedirect.com/science/article/pii/S0966636209003373

[10] Tang S, Wang X Y, Lü X T et al. Histogram of oriented normal vectors for object recognition with a depth sensor[M]. ∥Lee K M, Matsushita Y, Rehg J M, et al. European conference on computer vision-ACCV 2012. Lecture notes in computer science. Berlin, Heidelberg: Springer, 7725, 525-538(2013).

[11] Huang C H. Research on algorithms of human action recognition based on videos[D]. Chengdu: University of Electric Science and Technology of China(2016).

[12] Wang J. Human activity recognition using multiple instance learning[J]. Information Technology, 40, 65-70(2016).

[13] Liu Z, Dong S D. Study of human action recognition by using skeleton motion information in depth video[J]. Computer Applications and Software, 34, 189-192, 219(2017).

[14] Zhang Y J, Wang H M, Fu X H et al. Identification of steel plate damage position based on particle swarm support vector machine[J]. Chinese Journal of Lasers, 44, 1006006(2017).

[15] Liu F, Shen T S, Ma X X. Convolutional neural network based multi-band ship target recognition with feature fusion[J]. Acta Optica Sinica, 37, 1015002(2017).

[16] Bi L H, Liu Y C. Plant leaf image recognition based on improved neural network algorithm[J]. Laser & Optoelectronics Progress, 54, 121102(2017).

[17] Barnich O, van Droogenbroeck M. ViBe: a universal background subtraction algorithm for video sequences[J]. IEEE Transactions on Image Processing, 20, 1709-1724(2011). http://www.ncbi.nlm.nih.gov/pubmed/21189241

[18] Wang H Z, Suter D. A consensus-based method for tracking: modelling background scenario and foreground appearance[J]. Pattern Recognition, 40, 1091-1105(2007). http://dl.acm.org/citation.cfm?id=1219575

[19] Hofmann M, Tiefenbacher P, Rigoll G. Background segmentation with feedback:the pixel-based adaptive segmenter. [C]∥2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, June 16-21, 2012, Providence, RI, USA. New York: IEEE, 38-43(2012).

[20] Benenson R, Omran M, Hosang J et al. Ten years of pedestrian detection, what have we learned?[M]. ∥Agapito L, Bronstein M, Rother C. European conference on computer vision-ECCV 2014 Workshops. Lecture notes in computer science. Cham: Springer, 8926, 613-627(2015).

[21] Li J N, Liang X D, Shen S M et al. Scale-aware fast R-CNN for pedestrian detection[J]. IEEE Transactions on Multimedia, 20, 985-996(2018).

[22] Zhang L L, Lin L, Liang X D et al. Is faster R-CNN doing well for pedestrian detection?[M]. ∥Leibe B, Matas J, Sebe N, et al. European conference on computer vision-ECCV 2016. Lecture notes in computer science. Cham: Springer, 9906, 443-457(2016).

[23] LeCun Y, Kavukcuoglu K, Farabet C. Convolutional networks and applications in vision. [C]∥Proceedings of 2010 IEEE International Symposium on Circuits and Systems, May 30-June 2, 2010, Paris, France. New York: IEEE, 253-256(2010).

[24] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks. [C]∥Advances in neural information processing systems 25 (NIPS 2012), December 3-8, 2012, Harrahs and Harveys, Lake Tahoe. New York: NIPS, 1097-1105(2012).

[25] Girshick R, Donahue J, Darrell T et al. Rich feature hierarchies for accurate object detection and semantic segmentation. [C]∥2014 IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2014, Columbus, OH, USA. New York: IEEE, 580-587(2014).

[26] Farabet C, Couprie C, Najman L et al. Learning hierarchical features for scene labeling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 1915-1929(2013). http://ieeexplore.ieee.org/document/6338939/

[27] Schroff F, Kalenichenko D, Philbin J. FaceNet:a unified embedding for face recognition and clustering. [C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 815-823(2015).

[28] Yu S Q, Tan D L, Tan T N. A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition. [C]∥18th International Conference on Pattern Recognition (ICPR'06), August 20-24, 2006, Hong Kong, China. New York: IEEE, 441-444(2006).

[29] 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, November 3-7, 2014, Orlando, Florida, USA. New York: ACM, 675-678(2014).