激光与光电子学进展, 2019, 56 (21): 211503, 网络出版: 2019-11-02   

基于卷积神经网络的足跟着地事件检测算法 下载: 570次

Heel-Strike Event Detection Algorithm Based on Convolutional Neural Networks
作者单位
1 中国人民公安大学刑事科学技术学院, 北京 100038
2 上海市现场物证重点实验室, 上海 200083
引用该论文

李卓容, 王凯旋, 何欣龙, 糜忠良, 唐云祁. 基于卷积神经网络的足跟着地事件检测算法[J]. 激光与光电子学进展, 2019, 56(21): 211503.

Zhuorong Li, Kaixuan Wang, Xinlong He, Zhongliang Mi, Yunqi Tang. Heel-Strike Event Detection Algorithm Based on Convolutional Neural Networks[J]. Laser & Optoelectronics Progress, 2019, 56(21): 211503.

参考文献

[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, 2014, 14(2): 3362-3394.

[2] GirouxM, MoissenetF, Dumas R. EMG-based validation of musculo-skeletal models for gait analysis[J].Computer Methods in Biomechanics and BiomedicalEngineering, 2013, 16 sup1:152- 154.

[3] RoseJ, Gamble JG. Human walking[M]. 2nd ed. Baltimore: Williams & Wilkins, 1994.

[4] YangC, UgbolueU, CarseB, 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, 2013: 3128- 3131.

[5] Huang BF, ChenM, ShiX, 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, 2007: 579- 584.

[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, 2008, 28(3): 420-426.

[7] HeliotR, Pissard-GibolletR, EspiauB, 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, 2005: 686- 691.

[8] Williamson R, Andrews B J. Gait event detection for FES using accelerometers and supervised machine learning[J]. IEEE Transactions on Rehabilitation Engineering, 2000, 8(3): 312-319.

[9] Hanlon M, Anderson R. Real-time gait event detection using wearable sensors[J]. Gait & Posture, 2009, 30(4): 523-527.

[10] TangS, Wang XY, Lü XT, 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, 2013, 7725: 525- 538.

[11] 黄成挥. 基于视频的人体行为识别算法研究[D]. 成都: 电子科技大学, 2016.

    Huang CH. Research on algorithms of human action recognition based on videos[D]. Chengdu: University of Electric Science and Technology of China, 2016.

[12] 王军. 基于多示例学习法的人体行为识别[J]. 信息技术, 2016, 40(7): 65-70.

    Wang J. Human activity recognition using multiple instance learning[J]. Information Technology, 2016, 40(7): 65-70.

[13] 刘智, 董世都. 利用深度视频中的关节运动信息研究人体行为识别[J]. 计算机应用与软件, 2017, 34(2): 189-192, 219.

    Liu Z, Dong S D. Study of human action recognition by using skeleton motion information in depth video[J]. Computer Applications and Software, 2017, 34(2): 189-192, 219.

[14] 张燕君, 王会敏, 付兴虎, 等. 基于粒子群支持向量机的钢板损伤位置识别[J]. 中国激光, 2017, 44(10): 1006006.

    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, 2017, 44(10): 1006006.

[15] 刘峰, 沈同圣, 马新星. 特征融合的卷积神经网络多波段舰船目标识别[J]. 光学学报, 2017, 37(10): 1015002.

    Liu F, Shen T S, Ma X X. Convolutional neural network based multi-band ship target recognition with feature fusion[J]. Acta Optica Sinica, 2017, 37(10): 1015002.

[16] 毕立恒, 刘云潺. 基于改进神经网络算法的植物叶片图像识别研究[J]. 激光与光电子学进展, 2017, 54(12): 121102.

    Bi L H, Liu Y C. Plant leaf image recognition based on improved neural network algorithm[J]. Laser & Optoelectronics Progress, 2017, 54(12): 121102.

[17] Barnich O, van Droogenbroeck M. ViBe: a universal background subtraction algorithm for video sequences[J]. IEEE Transactions on Image Processing, 2011, 20(6): 1709-1724.

[18] Wang H Z, Suter D. A consensus-based method for tracking: modelling background scenario and foreground appearance[J]. Pattern Recognition, 2007, 40(3): 1091-1105.

[19] HofmannM, TiefenbacherP, RigollG. 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, 2012: 38- 43.

[20] BenensonR, OmranM, HosangJ,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, 2015, 8926: 613- 627.

[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, 2018, 20(4): 985-996.

[22] Zhang LL, LinL, Liang XD, 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, 2016, 9906: 443- 457.

[23] LeCunY, KavukcuogluK, FarabetC. 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, 2010: 253- 256.

[24] KrizhevskyA, SutskeverI, Hinton GE. 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, 2012: 1097- 1105.

[25] GirshickR, DonahueJ, DarrellT, 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, 2014: 580- 587.

[26] Farabet C, Couprie C, Najman L, et al. Learning hierarchical features for scene labeling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(8): 1915-1929.

[27] SchroffF, KalenichenkoD, PhilbinJ. 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, 2015: 815- 823.

[28] Yu SQ, Tan DL, Tan TN. 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, 2006: 441- 444.

[29] Jia YQ, ShelhamerE, DonahueJ, 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, 2014: 675- 678.

李卓容, 王凯旋, 何欣龙, 糜忠良, 唐云祁. 基于卷积神经网络的足跟着地事件检测算法[J]. 激光与光电子学进展, 2019, 56(21): 211503. Zhuorong Li, Kaixuan Wang, Xinlong He, Zhongliang Mi, Yunqi Tang. Heel-Strike Event Detection Algorithm Based on Convolutional Neural Networks[J]. Laser & Optoelectronics Progress, 2019, 56(21): 211503.

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