Citation: | Liu Xiaoyang, Liu Jinqiang, Zheng Haolin. Gait recognition method of coal mine personnel based on Two-Stream neural network[J]. Journal of Mining Science and Technology, 2021, 6(2): 218-227. doi: 10.19606/j.cnki.jmst.2021.02.010 |
[1] |
柴艳妹, 夏天, 韩文英, 等. 步态识别研究进展[J]. 计算机科学, 2012, 39(6): 10-15, 46. doi: 10.3969/j.issn.1002-137X.2012.06.003
Chai Yanmei, Xia Tian, Han Wenying, et al. State-of-the-art on gait recognition[J]. Computer Science, 2012, 39(6): 10-15, 46. doi: 10.3969/j.issn.1002-137X.2012.06.003
|
[2] |
向斓. 基于关节点提取和多视角步态识别算法[D]. 武汉: 武汉理工大学, 2008.
|
[3] |
张善文, 张传雷, 黄文准. 基于最大最小判别映射的煤矿井下人员身份鉴别方法[J]. 煤炭学报, 2013, 38(10): 1894-1899. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB201310035.htm
Zhang Shangwen, Zhang Chuanlei, Huang Wenzhun. Personnel identification in mine underground based on maximin discriminant projection[J]. Journal of China Coal Society, 2013, 38(10): 1894-1899. https://www.cnki.com.cn/Article/CJFDTOTAL-MTXB201310035.htm
|
[4] |
刘晓阳, 梁涛, 胡乔森. 基于无线传感器网络的井下容错性定位方法[J]. 矿业科学学报, 2017, 2(2): 167-174. http://kykxxb.cumtb.edu.cn/CN/abstract/abstract60.shtml
Liu Xiaoyang, Liang Tao, Hu Qiaosen. Fault-tolerant localization method for underground mine based on wireless sensor network[J]. Journal of Mining Science and Technology, 2017, 2(2): 167-174. http://kykxxb.cumtb.edu.cn/CN/abstract/abstract60.shtml
|
[5] |
张帆, 李亚杰, 孙晓辉. 无线感知与视觉融合的井下目标跟踪定位方法[J]. 矿业科学学报, 2018, 3(5): 484-491. http://kykxxb.cumtb.edu.cn/CN/abstract/abstract175.shtml
Zhang Fan, Li Yajie, Sun Xiaohui. A novel method of mine target tracking and location based on wireless sensor and visual recognition[J]. Journal of Mining Science and Technology, 2018, 3(5): 484-491. http://kykxxb.cumtb.edu.cn/CN/abstract/abstract175.shtml
|
[6] |
吴雅琴, 杨硕, 师兰兰. 基于位置指纹与PDR融合的室内定位算法研究[J]. 矿业科学学报, 2019, 4(5): 448-454. http://kykxxb.cumtb.edu.cn/CN/abstract/abstract245.shtml
Wu Yaqin, Yang Shuo, Shi Lanlan. Research on indoor positioning algorithm based on location fingerprint and PDR[J]. Journal of Mining Science and Technology, 2019, 4(5): 448-454. http://kykxxb.cumtb.edu.cn/CN/abstract/abstract245.shtml
|
[7] |
赵喜玲, 张晓惠. 基于动态特征和静态特征融合的步态识别方法[J]. 湘潭大学自然科学学报, 2017, 39(004): 89-91. https://www.cnki.com.cn/Article/CJFDTOTAL-XYDZ201704020.htm
Zhao xiling Zhang Xiaohui. Gait recognition based on dynamic and static feature fusion[J]. Natural Science Journal of Xiangtan University, 2017, 39(4): 89-90. https://www.cnki.com.cn/Article/CJFDTOTAL-XYDZ201704020.htm
|
[8] |
Liu Lingfeng, Jia Wei, Zhu Yihai.Gait recognition using hough transform and principal component analysis[C]//Emerging Intelligent Computing Technology and Applications.Berlin, Heidelberg: Springer Berlin Heidelberg, 2009: 363-370.
|
[9] |
Wu Zifeng, Huang Yongzhen, Wang Liang et al. A Comprehensive study on cross-view gait based human identification with deepcnns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(2): 209-226. doi: 10.1109/TPAMI.2016.2545669
|
[10] |
Yu Shiqi, Chen Haifeng, Reyes E B G, et al. Gaitgan: invariant gait feature extraction using generative adversarial networks[C]// In Proceedings of the 2017 IEEE Conference Computer Vision and Pattern Recognition(CVPRW).Honolulu, HI, 2017: 532-539.
|
[11] |
Chao Hanqing, He Yiwei, Zhang Junping, et al. Gaitset: regarding gait as a set for cross-view gait recognition[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33: 8126-8133. doi: 10.1609/aaai.v33i01.33018126
|
[12] |
He Kaiming, Zhang Xiangyu.Deep residual learning for image recognition[C]// In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Miami, FL: 2016: 770-778.
|
[13] |
Masci J, Meier U, Ciresan D, et al. Stacked convolutional auto-encoders for hierarchical feature extraction[J]. Lecture Notes in Computer Science, 2011, 6791: 52-59. doi: 10.1007/978-3-642-21735-7_7.pdf
|
[14] |
Tao Yiting, Xu Miaozhong, Zhong Yanfei. Gan-assisted two-stream neural network for high-resolution remote sensing image classification[J]. Remote Sensing, 2017, 9(12): 1328-1357. doi: 10.3390/rs9121328
|
[15] |
Han Ju, Bhanu B.Individual recognition using gait energy image[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(2): 316-322. doi: 10.1109/TPAMI.2006.38
|
[16] |
Glorot X, Bengio Y.Understanding the difficulty of training deep feedforward neural networks[J]. Journal of Machine Learning Research, 2010, 9(1): 249-256. http://www.researchgate.net/publication/215616968_Understanding_the_difficulty_of_training_deep_feedforward_neural_networks
|
[17] |
He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification[C]// ICCV'15: Proceedings of the 2015 IEEE International Conference on Computer Vision(ICCV)IEEE, 2015: 1026-1034.
|
[18] |
Liu Yishu, Liu Yingbin, Ding Liwang. Scene classification based on two-stage deep feature fusion[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15(2): 183-186. doi: 10.1109/LGRS.2017.2779469
|
[19] |
李昊璇, 王芬. 基于深度残差网络的人脸关键点检测[J]. 测试技术学报, 2019, 33(6): 516-519, 546. doi: 10.3969/j.issn.1671-7449.2019.06.012
Li Haoxuan, Wang Fen. Facial keypoints detection based on deep residual network[J]. Journal of Test and Measurement Technology, 2019, 33(6): 516-519, 546. doi: 10.3969/j.issn.1671-7449.2019.06.012
|
[20] |
He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Identity mappings in deep residual networks[C]//Computer Vision-ECCV 2016.Cham: Springer International Publishing, 2016: 630-645.
|
[21] |
Yoo D, Kim N, Park S, et al. Pixel-level domain transfer[EB/OL]. [2016-03-24]arXiv: 1603.07442[cs.CV]. https://arxiv.org/abs/1603.07442.
|
[22] |
Yu Shiqi, Tan Daoliang, Tan Tieniu.A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition[C]// In Proceedings of the IEEE of 18th International Conference on Pattern Recognition(ICPR), Hong Kong: 2006: 441-444.
|