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A Fast Vision-inertial Odometer Based on Line Midpoint Descriptor 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 4, 页码: 667-669
作者:  Wen-Kuan Li;  Hao-Yuan Cai;  Sheng-Lin Zhao;  Ya-Qian Liu;  Chun-Xiu Liu
Adobe PDF(10350Kb)  |  收藏  |  浏览/下载:181/40  |  提交时间:2021/07/20
High efficiency  visual-inertial odometry (VIO)  non-linear optimization  points and lines  sliding window  
Learning Deep RGBT Representations for Robust Person Re-identification 期刊论文
International Journal of Automation and Computing, 2021, 卷号: 18, 期号: 3, 页码: 443-456
作者:  Ai-Hua Zheng;  Zi-Han Chen;  Cheng-Long Li;  Jin Tang;  Bin Luo
Adobe PDF(1832Kb)  |  收藏  |  浏览/下载:270/56  |  提交时间:2021/05/24
Person re-identification (Re-ID)  thermal infrared  generative networks  attention  deep learning  
PLS-CCA Heterogeneous Features Fusion-based Low-resolution Human Detection Method for Outdoor Video Surveillance 期刊论文
International Journal of Automation and Computing, 2017, 卷号: 14, 期号: 2, 页码: 136-146
作者:  Hong-Kai Chen;  Xiao-Guang Zhao;  Shi-Ying Sun;  Min Tan
浏览  |  Adobe PDF(13495Kb)  |  收藏  |  浏览/下载:211/39  |  提交时间:2021/02/23
Low-resolution human detection  partial least squares  canonical correlation analysis  heterogeneous features  outdoor video surveillance.  
A Survey of Scene Understanding by Event Reasoning in Autonomous Driving 期刊论文
International Journal of Automation and Computing, 2018, 卷号: 15, 期号: 3, 页码: 249-266
作者:  Jian-Ru Xue;  Jian-Wu Fang;  Pu Zhang
浏览  |  Adobe PDF(2242Kb)  |  收藏  |  浏览/下载:109/38  |  提交时间:2021/02/23
Autonomous vehicle  scene understanding  event reasoning  intention prediction  scene representation.  
Large-scale 3D Semantic Mapping Using Stereo Vision 期刊论文
International Journal of Automation and Computing, 2018, 卷号: 15, 期号: 2, 页码: 194-206
作者:  Yi Yang;  Fan Qiu1 Hao Li;  Lu Zhang;  Mei-Ling Wang;  Meng-Yin Fu
浏览  |  Adobe PDF(70896Kb)  |  收藏  |  浏览/下载:164/50  |  提交时间:2021/02/23
Semantic map  stereo vision  motion segmentation  visual odometry  simultaneous localization and mapping (SLAM).  
Predictive Adaptive Kalman Filter and Its Application to INS/UWB-integrated Human Localization with Missing UWB-based Measurements 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 5, 页码: 604-613
作者:  Yuan Xu;  Tao Shen;  Xi-Yuan Chen;  Li-Li Bu;  Ning Feng
浏览  |  Adobe PDF(4479Kb)  |  收藏  |  浏览/下载:155/48  |  提交时间:2021/02/22
Indoor human localization  tightly-coupled model  predictive filtering  Kalman filter  missing data.  
Pedestrian Height Estimation and 3D Reconstruction Using Pixel-resolution Mapping Method Without Special Patterns 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 4, 页码: 449-461
作者:  Bing-Xing Wu;  Suat Utku Ay;  Ahmed Abdel-Rahim
浏览  |  Adobe PDF(6241Kb)  |  收藏  |  浏览/下载:116/35  |  提交时间:2021/02/22
Traffic monitoring application  spatial resolution  pixel-resolution mapping (P-RM) method  3D information  pedestrian height estimation.  
A Survey of the Research Status of Pedestrian Dead Reckoning Systems Based on Inertial Sensors 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 1, 页码: 65-83
作者:  Yuan Wu;  Hai-Bing Zhu;  Qing-Xiu Du;  Shu-Ming Tang
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Inertial measurement unit (IMU)  pedestrian dead-reckoning  indoor navigation  technical route  general framework.  
Effective Crowd Anomaly Detection Through Spatio-temporal Texture Analysis 期刊论文
International Journal of Automation and Computing, 2019, 卷号: 16, 期号: 1, 页码: 27-39
作者:  Yu Hao;  Zhi-Jie Xu;  Ying Liu;  Jing Wang;  Jiu-Lun Fan
浏览  |  Adobe PDF(1521Kb)  |  收藏  |  浏览/下载:213/56  |  提交时间:2021/02/22
Crowd behavior  spatial-temporal texture  gray level co-occurrence matrix  information entropy.  
Automatic “Ground Truth” Annotation and Industrial Workpiece Dataset Generation for Deep Learning 期刊论文
International Journal of Automation and Computing, 2020, 卷号: 17, 期号: 4, 页码: 539-550
作者:  Fu-Qiang Liu;  Zong-Yi Wang
浏览  |  Adobe PDF(1144Kb)  |  收藏  |  浏览/下载:144/51  |  提交时间:2021/02/22
Deep learning  dataset generation  automatic annotation  neural networks  industrial workpiece dataset.