{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T05:20:05Z","timestamp":1788844805137,"version":"build-2803163510"},"reference-count":41,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T00:00:00Z","timestamp":1577145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 61573091"],"award-info":[{"award-number":["No. 61573091"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["No. N182608003, No. N172608005"],"award-info":[{"award-number":["No. N182608003, No. N172608005"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005047","name":"Natural Science Foundation of Liaoning Province","doi-asserted-by":"publisher","award":["20180520006"],"award-info":[{"award-number":["20180520006"]}],"id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Person tracking is an important issue in both computer vision and robotics. However, most existing person tracking methods using 3D point cloud are based on the Bayesian Filtering framework which are not robust in challenging scenes. In contrast with the filtering methods, in this paper, we propose a neural network to cope with person tracking using only 3D point cloud, named Point Siamese Network (PSN). PSN consists of two input branches named template and search, respectively. After finding the target person (by reading the label or using a detector), we get the inputs of the two branches and create feature spaces for them using feature extraction network. Meanwhile, a similarity map based on the feature space is proposed between them. We can obtain the target person from the map. Furthermore, we add an attention module to the template branch to guide feature extraction. To evaluate the performance of the proposed method, we compare it with the Unscented Kalman Filter (UKF) on 3 custom labeled challenging scenes and the KITTI dataset. The experimental results show that the proposed method performs better than UKF in robustness and accuracy and has a real-time speed. In addition, we publicly release our collected dataset and the labeled sequences to the research community.<\/jats:p>","DOI":"10.3390\/s20010143","type":"journal-article","created":{"date-parts":[[2019,12,24]],"date-time":"2019-12-24T10:28:43Z","timestamp":1577183323000},"page":"143","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Point Siamese Network for Person Tracking Using 3D Point Clouds"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5302-0484","authenticated-orcid":false,"given":"Yubo","family":"Cui","sequence":"first","affiliation":[{"name":"Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3887-3141","authenticated-orcid":false,"given":"Zheng","family":"Fang","sequence":"additional","affiliation":[{"name":"Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sifan","family":"Zhou","sequence":"additional","affiliation":[{"name":"Faculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,24]]},"reference":[{"key":"ref_1","unstructured":"Luca, B., Jack, V., Stuart, G., Ondrej, M., and Philip, T. (July, January 26). Staple: Complementary Learners for Real-Time Tracking. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA."},{"key":"ref_2","unstructured":"Luca, B., Jack, V., Jo\u00e3o, F.H., Andrea, V., and Philip, T. (2016, January 8\u201316). Fully-convolutional siamese networks for object tracking. Proceedings of the ECCV 2016 Workshops, Amsterdam, The Netherlands."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, B., Yan, J., Wu, W., Zhu, Z., and Hu, X. (2018, January 18\u201322). High Performance Visual Tracking with Siamese Region Proposal Network. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00935"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Huang, L., Chen, S., Zhang, J., Cheng, B., and Liu, M. (2017). Real-Time Motion Tracking for Indoor Moving Sphere Objects with a LiDAR Sensor. Sensors, 17.","DOI":"10.3390\/s17091932"},{"key":"ref_5","unstructured":"John, S., Brian, V., and Joel, H. (September, January 29). Tracking People with a 360-Degree Lidar. Proceedings of the 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance, Boston, MA, USA."},{"key":"ref_6","unstructured":"Zhi, Y., Tom, D., and Nicola, B. (2017, January 24\u201328). Online learning for human classification in 3D LiDAR-based tracking. Proceedings of the 2017 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, Canada."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ma, Y., Anderson, J., Crouch, S., and Shan, J. (2019). Moving Object Detection and Tracking with Doppler LiDAR. Remote Sens., 11.","DOI":"10.3390\/rs11101154"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Razlaw, J., Quenzel, J., and Behnke, S. (2019). Detection and Tracking of Small Objects in Sparse 3D Laser Range Data. arXiv.","DOI":"10.1109\/ICRA.2019.8794204"},{"key":"ref_9","unstructured":"Kiyosumi, K., Takeo, M., Akihiro, W., Takashi, N., and Jun, M. (2011, January 5\u20139). Pedestrian recognition using high-definition LIDAR. Proceedings of the 2011 IEEE Intelligent Vehicles Symposium (IV), Baden-Baden, Germany."},{"key":"ref_10","unstructured":"Charles, R., Li, Y., Hao, S., and Leonidas, J. (2017, January 4\u20139). Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Qi, C.R., Liu, W., Wu, C., Su, H., and Guibas, L.J. (2018, January 18\u201322). Frustum PointNets for 3D Object Detection from RGB-D Data. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00102"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Shi, S., Wang, X., and Li, H. (2019, January 16\u201320). PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00086"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, W., Yu, R., Huang, Q., and Neumann, U. (2018, January 18\u201322). Sgpn: Similarity group proposal network for 3d point cloud instance segmentation. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition(CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00272"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Frossard, D., and Urtasun, R. (2018, January 21\u201326). End-to-end learning of multi-sensor 3d tracking by detection. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8462884"},{"key":"ref_15","unstructured":"Daniel, M., and Sebastian, S. (October, January 28). Voxnet: A 3d convolutional neural network for real-time object recognition. Proceedings of the 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Charles, R., Hao, S., Kaichun, M., and Leonidas, J. (2017, January 22\u201325). PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.16"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bolme, D.S., Beveridge, J.R., and Lui, Y.M. (2010, January 15\u201317). Visual object tracking using adaptive correlation filters. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA.","DOI":"10.1109\/CVPR.2010.5539960"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1109\/TPAMI.2014.2345390","article-title":"High-speed tracking with kernelized correlation filters","volume":"37","author":"Rui","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","unstructured":"Martin, D., Gustav, H., Fahad, S., and Michael, F. (2014, January 1\u20135). Accurate scale estimation for robust visual tracking. Proceedings of the British Machine Vision Conference(BMVC), Nottingham, UK."},{"key":"ref_20","unstructured":"Martin, D., Andreas, R., Fahad, S., and Michael, F. (2016, January 8\u201316). Beyond Correlation Filters: Learning Continuous Convolution Operators for Visual Tracking. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands."},{"key":"ref_21","unstructured":"Martin, D., Goutam, B., Fahad, S., and Michael, F. (2017, January 22\u201325). Eco: Efficient convolution operators for tracking. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA."},{"key":"ref_22","unstructured":"Hyeonseob, N., Mooyeol, B., and Bohyung, H. (2016). Modeling and Propagating CNNs in a Tree Structure for Visual Tracking. arXiv."},{"key":"ref_23","unstructured":"David, H., Sebastian, T., and Silvio, S. (2016, January 8\u201316). Learning to track at 100 fps with deep regression networks. Proceedings of the Computer Vision (ECCV 2016), Cham, Amsterdam, The Netherlands."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Wang, Q., Li, B., Wu, W., Yan, J., and Hu, W. (2018, January 8\u201314). Distractor-Aware Siamese Networks for Visual Object Tracking. Proceedings of the 15th European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01240-3_7"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Li, B., Wu, W., Wang, Q., Zhang, F., Xing, J., and Yan, J. (2019, January 16\u201320). SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00441"},{"key":"ref_27","unstructured":"Luciano, S., Matthias, L., and Kai O, A. (2011, January 9\u201313). Tracking people in 3D using a bottom-up top-down detector. Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1368","DOI":"10.1109\/TITS.2015.2502325","article-title":"Density Enhancement-Based Long-Range Pedestrian Detection Using 3-D Range Data","volume":"17","author":"Li","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_29","unstructured":"Li, Y., Pirk, S., Su, H., Qi, C.R., and Guibas, L.J. (2016, January 5\u201310). Fpnn: Field probing neural networks for 3d data. Proceedings of the Advances in Neural Information Processing Systems, Barcelona, Spain."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Luo, W., Yang, B., and Urtasun, R. (2018, January 18\u201322). Fast and furious: Real time end-to-end 3d detection, tracking and motion forecasting with a single convolutional net. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00376"},{"key":"ref_31","unstructured":"Silvio, G., Jesus, Z., and Bernard, G. (2019, January 16\u201320). Leveraging shape completion for 3d siamese tracking. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Simon, M., Milz, S., Amende, K., and Gross, H.M. (2018). Complex-YOLO: Real-time 3D Object Detection on Point Clouds. arXiv.","DOI":"10.1109\/CVPRW.2019.00158"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Koide, K., Miura, J., and Menegatti, E. (2019). A Portable 3D LIDAR-based System for Long-term and Wide-area People Behavior Measurement. Int. J. Adv. Robot. Syst.","DOI":"10.1177\/1729881419841532"},{"key":"ref_34","unstructured":"Marcel, H., Benedikt, J., Nicolai, W., Jens, H., and Dietrich, P. (2014, January 14\u201318). Confidence-based pedestrian tracking in unstructured environments using 3d laser distance measurements. Proceedings of the 2014 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL, USA."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1023\/A:1007614523901","article-title":"Improved boosting algorithms using confidencerated predictions","volume":"37","author":"Schapire","year":"1999","journal-title":"Mach. Learn."},{"key":"ref_36","unstructured":"Hang, S., Subhransu, M., Evangelos, K., and Erik, G. (2015, January 13\u201316). Multi-view convolutional neural networks for 3d shape recognition. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile."},{"key":"ref_37","unstructured":"Andreas, G., Philip, L., and Raquel, U. (2012, January 16\u201321). Are we ready for autonomous driving? the kitti vision benchmark suite. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA."},{"key":"ref_38","unstructured":"Diederik, K., and Jimmy, B. (2014, January 14\u201316). Adam: A method for stochastic optimization. Proceedings of the International Conference on Learning Representations, Banff, AB, Canada."},{"key":"ref_39","unstructured":"Morgan, Q., Brian, G., Ken, C., Josh, F., Tully, F., Jeremy, L., Eric, B., Rob, W., and Andrew, N. (2009, January 12\u201317). Ros: An open-source robot operating system. Proceedings of the ICRA Workshop on Open Source Software, Kobe, Japan."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2137","DOI":"10.1109\/TPAMI.2016.2516982","article-title":"A novel performance evaluation methodology for single-target trackers","volume":"38","author":"Matej","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","unstructured":"Arsalan, M., Dragomir, A., John, F., and Jana, K. (2017, January 22\u201325). 3d bounding box estimation using deep learning and geometry. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/143\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:45:22Z","timestamp":1760190322000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/1\/143"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,12,24]]},"references-count":41,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1]]}},"alternative-id":["s20010143"],"URL":"https:\/\/doi.org\/10.3390\/s20010143","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,12,24]]}}}