{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T14:10:19Z","timestamp":1778335819137,"version":"3.51.4"},"reference-count":43,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2021,2,7]],"date-time":"2021-02-07T00:00:00Z","timestamp":1612656000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100017329","name":"Foundation of Science and Technology on Near-Surface Detection Laboratory","doi-asserted-by":"publisher","award":["No.6142414180207"],"award-info":[{"award-number":["No.6142414180207"]}],"id":[{"id":"10.13039\/100017329","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Pedestrian detection plays an essential role in the navigation system of autonomous vehicles. Multisensor fusion-based approaches are usually used to improve detection performance. In this study, we aimed to develop a score fusion-based pedestrian detection algorithm by integrating the data of two light detection and ranging systems (LiDARs). We first evaluated a two-stage object-detection pipeline for each LiDAR, including object proposal and fine classification. The scores from these two different classifiers were then fused to generate the result using the Bayesian rule. To improve proposal performance, we applied two features: the central points density feature, which acts as a filter to speed up the process and reduce false alarms; and the location feature, including the density distribution and height difference distribution of the point cloud, which describes an object\u2019s profile and location in a sliding window. Extensive experiments tested in KITTI and the self-built dataset show that our method could produce highly accurate pedestrian detection results in real-time. The proposed method not only considers the accuracy and efficiency but also the flexibility for different modalities.<\/jats:p>","DOI":"10.3390\/s21041159","type":"journal-article","created":{"date-parts":[[2021,2,10]],"date-time":"2021-02-10T04:33:46Z","timestamp":1612931626000},"page":"1159","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A Pedestrian Detection Algorithm Based on Score Fusion for Multi-LiDAR Systems"],"prefix":"10.3390","volume":"21","author":[{"given":"Tao","family":"Wu","sequence":"first","affiliation":[{"name":"College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Ye","sequence":"additional","affiliation":[{"name":"College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Ding","sequence":"additional","affiliation":[{"name":"Science and Technology on Near-Surface Detection Laboratory, Wuxi 214035, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1093\/her\/cyp003","article-title":"Understanding the role of self-identity in habitual risky behaviours: Pedestrian road-crossing decisions across the lifespan","volume":"24","author":"Holland","year":"2009","journal-title":"Health Educ. Res."},{"key":"ref_2","unstructured":"Shashua, A., Gdalyahu, Y., and Hayun, G. (2004, January 14\u201317). Pedestrian detection for driving assistance systems: Single-frame classification and system level performance. Proceedings of the IEEE Intelligent Vehicles Symposium, Parma, Italy."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, Z., Wang, K., Li, L., and Wang, F.Y. (2006, January 13\u201315). A review on vision-based pedestrian detection for intelligent vehicles. Proceedings of the 2006 IEEE International Conference on Vehicular Electronics and Safety, Shanghai, China.","DOI":"10.1109\/ICVES.2006.371554"},{"key":"ref_4","unstructured":"Dalal, N., and Triggs, B. (2005, January 20\u201325). Histograms of oriented gradients for human detection. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), San Diego, CA, USA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MC.2014.42","article-title":"Object detection with discriminatively trained part-based models","volume":"47","author":"Forsyth","year":"2014","journal-title":"Computer"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tran, D., Bourdev, L., Fergus, R., Torresani, L., and Paluri, M. (2015, January 13\u201316). Learning spatiotemporal features with 3D convolutional networks. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.510"},{"key":"ref_7","unstructured":"Li, B., Zhang, T., and Xia, T. (2016). Vehicle detection from 3D lidar using fully convolutional network. Robot. Sci. Syst., 12."},{"key":"ref_8","unstructured":"Engelcke, M., Rao, D., Wang, D.Z., Tong, C.H., and Posner, I. (June, January 29). Vote3Deep: Fast object detection in 3D point clouds using efficient convolutional neural networks. Proceedings of the IEEE International Conference on Robotics and Automation, Singapore."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Prokhorov, D. (2010). A Convolutional Learning System for Object Classification in 3-D Lidar Data, IEEE Press.","DOI":"10.1109\/TNN.2010.2044802"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Beltr\u00e1n, J., Guindel, C., Moreno, F.M., Cruzado, D., Garc\u00eda, F., and De La Escalera, A. (2018, January 4\u20137). Birdnet: A 3d object detection framework from lidar information. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems, Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569311"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2270","DOI":"10.1016\/j.patcog.2005.01.012","article-title":"Score normalization in multimodal biometric systems","volume":"38","author":"Jain","year":"2005","journal-title":"Pattern Recognit."},{"key":"ref_12","unstructured":"Ross, A.A. (2006). Handbook of Multibiometrics, Springer."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tu, X., Zhang, J., Luo, R., Wang, K., Zeng, Q., Zhou, Y., Yu, Y., and Du, S. (2020). Reconstruction of high-precision semantic map. Sensors, 20.","DOI":"10.3390\/s20216264"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1002\/rob.21430","article-title":"Moving object detection with laser scanners","volume":"30","author":"Mertz","year":"2013","journal-title":"J. Field Robot."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1002\/rob.21609","article-title":"LiDAR Based Negative Obstacle Detection for Field Autonomous Land Vehicles","volume":"33","author":"Shang","year":"2016","journal-title":"J. Field Robot."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"696","DOI":"10.1002\/rob.20312","article-title":"LIDAR and vision-based pedestrian detection system","volume":"26","author":"Premebida","year":"2009","journal-title":"J. Field Robot."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s10514-009-9113-3","article-title":"Finding multiple lanes in urban road networks with vision and lidar","volume":"26","author":"Huang","year":"2009","journal-title":"Auton. Robot."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4901","DOI":"10.1109\/JSEN.2020.2966034","article-title":"Fusion of 3D LIDAR and Camera Data for Object Detection in Autonomous Vehicle Applications","volume":"20","author":"Zhao","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Oh, S.I., and Kang, H.B. (2017). Object detection and classification by decision-level fusion for intelligent vehicle systems. Sensors, 17.","DOI":"10.3390\/s17010207"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"31464","DOI":"10.3390\/s151229867","article-title":"Probabilistic multi-sensor fusion based indoor positioning system on a mobile device","volume":"15","author":"He","year":"2015","journal-title":"Sensors"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ahmad Yousef, K.M., Mohd, B.J., Al-Widyan, K., and Hayajneh, T. (2017). Extrinsic calibration of camera and 2D laser sensors without overlap. Sensors, 17.","DOI":"10.3390\/s17102346"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, W. (2017). Lidar-imu time delay calibration based on iterative closest point and iterated sigma point kalman filter. Sensors, 17.","DOI":"10.3390\/s17030539"},{"key":"ref_23","first-page":"634","article-title":"Sliding shapes for 3D object detection in depth images","volume":"Volume 8694","author":"Song","year":"2014","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wirges, S., Fischer, T., Stiller, C., and Frias, J.B. (2018, January 4\u20137). Object detection and classification in occupancy grid maps using deep convolutional networks. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems, Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569433"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ku, J., Mozifian, M., Lee, J., Harakeh, A., and Waslander, S.L. (2018, January 1\u20135). Joint 3D proposal generation and object detection from view aggregation. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8594049"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Chen, X., Ma, H., Wan, J., Li, B., and Xia, T. (2017, January 21\u201326). Multi-view 3D object detection network for autonomous driving. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.691"},{"key":"ref_27","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 21\u201326). PointNet: Deep learning on point sets for 3D classification and segmentation. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_28","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2017). PointNet++: Deep hierarchical feature learning on point sets in a metric space. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Simon, M., Amende, K., Kraus, A., Honer, J., S\u00e4mann, T., Kaulbersch, H., Milz, S., and Gross, H.M. (2019, January 16\u201320). Complexer-YOLO: Real-Time 3D object detection and tracking on semantic point clouds. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA.","DOI":"10.1109\/CVPRW.2019.00158"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Peterson, K., Ziglar, J., and Rybski, P.E. (2008, January 22\u201326). Fast feature detection and stochastic parameter estimation of road shape using multiple LIDAR. Proceedings of the 2008 IEEE\/RSJ International Conference on Intelligent Robots and Systems, Nice, France.","DOI":"10.1109\/IROS.2008.4651161"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"656103","DOI":"10.1117\/12.720513","article-title":"Night-time negative obstacle detection for off-road autonomous navigation","volume":"6561","author":"Rankin","year":"2007","journal-title":"Unmanned Syst. Technol. IX"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Larson, J., and Trivedi, M. (2011, January 5\u20137). Lidar based off-road negative obstacle detection and analysis. Proceedings of the IEEE Conference on Intelligent Transportation Systems, Washington, DC, USA.","DOI":"10.1109\/ITSC.2011.6083105"},{"key":"ref_33","first-page":"391","article-title":"Edge boxes: Locating object proposals from edges","volume":"Volume 8693","author":"Zitnick","year":"2014","journal-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_34","first-page":"1990","article-title":"Learning to segment object candidates","volume":"2015","author":"Pinheiro","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1007\/s10846-013-9889-4","article-title":"Gaussian-Process-Based Real-Time Ground Segmentation for Autonomous Land Vehicles","volume":"76","author":"Chen","year":"2014","journal-title":"J. Intell. Robot. Syst. Theory Appl."},{"key":"ref_36","unstructured":"Li, K.L., Huang, H.K., Tian, S.F., and Xu, W. (2003, January 5). Improving one-class SVM for anomaly detection. Proceedings of the International Conference on Machine Learning and Cybernetics, Xi\u2019an, China."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1443","DOI":"10.1162\/089976601750264965","article-title":"Estimating the support of a high-dimensional distribution","volume":"13","author":"Platt","year":"2001","journal-title":"Neural Comput."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Premebida, C., Ludwig, O., and Nunes, U. (2009, January 4\u20137). Exploiting LIDAR-based features on pedestrian detection in urban scenarios. Proceedings of the IEEE Conference on Intelligent Transportation Systems, St. Louis, MO, USA.","DOI":"10.1109\/ITSC.2009.5309697"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/978-3-642-13408-1_10","article-title":"Pedestrian detection and tracking using three-dimensional LADAR data","volume":"62","author":"Mertz","year":"2010","journal-title":"Springer Tracts Adv. Robot."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1007\/s11263-013-0627-y","article-title":"Rotational projection statistics for 3D local surface description and object recognition","volume":"105","author":"Guo","year":"2013","journal-title":"Int. J. Comput. Vis."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1109\/ICPR.1996.547205","article-title":"Combining classifiers","volume":"2","author":"Kittler","year":"1996","journal-title":"Proc. Int. Conf. Pattern Recognit."},{"key":"ref_42","unstructured":"Duda, R.O., Hart, P.E., and Stork, D.G. (2001). Pattern Classification, John Wiley & Sons. [2nd ed.]."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Geiger, A., Lenz, P., and Urtasun, R. (2012, January 16\u201321). Are we ready for autonomous driving? The KITTI vision benchmark suite. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1159\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:20:45Z","timestamp":1760160045000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/4\/1159"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,7]]},"references-count":43,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,2]]}},"alternative-id":["s21041159"],"URL":"https:\/\/doi.org\/10.3390\/s21041159","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,2,7]]}}}