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J-GLOBAL ID:202202259117534710   Reference number:22A2094774

A Study on Robust Pedestrian Tracking Method Using Partial 3D Point Clouds

公共空間における三次元点群の不完全性に対して堅牢な歩行者トラッキング手法
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Volume: 63  Issue:Page: 1361-1370 (WEB ONLY)  Publication year: Aug. 15, 2022 
JST Material Number: U0452A  ISSN: 1882-7764  Document type: Article
Article type: 原著論文  Country of issue: Japan (JPN)  Language: JAPANESE (JA)
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Pattern recognition 
Reference (14):
  • Abbas, M., Elhamshary, M., Rizk, H., Torki, M. and Youssef, M.: Wideep: WiFi-based Accurate and Robust Indoor Localization System using Deep Learning, Proc. IEEE International Conference on Pervasive Computing and Communications, pp.1-10 (2019).
  • Yamaguchi, H., Hiromori, A. and Higashino, T.: A Human Tracking and Sensing Platform for Enabling Smart City Applications, Proc. Workshop Program of the 19th International Conference on Distributed Computing and Networking, Workshops ICDCN ’18, pp.13:1-13:6, ACM (2018).
  • Alzantot, M. and Youssef, M.: UPTIME: Ubiquitous Pedestrian Tracking Using Mobile Phones, Proc. IEEE Wireless Communications and Networking Conference, pp.3204-3209 (2012).
  • Charles, R.Q., Su, H., Kaichun, M. and Guibas, L.J.: PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation, Proc. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.77-85 (2017).
  • Qi, C.R., Yi, L., Su, H. and Guibas, L.J.: PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space, Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S. and Garnett, R. (Eds.), Advances in Neural Information Processing Systems, Vol.30, pp.5099-5108, Curran Associates, Inc.(2017).
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