{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T17:19:02Z","timestamp":1791047942468,"version":"4.1.0"},"reference-count":44,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T00:00:00Z","timestamp":1764720000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development Program of Shandong","award":["2024CXGC010110"],"award-info":[{"award-number":["2024CXGC010110"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the widespread adoption of camera and LiDAR sensors, 3D multi-object tracking (MOT) technology has been extensively applied across numerous fields such as robotics, autonomous driving, and surveillance. However, existing 3D MOT methods still face significant challenges in addressing issues such as false detections, ghost trajectories, incorrect associations, and identity switches. To address these challenges, we propose a lightweight 3D multi-object tracking framework via collaborative camera and LiDAR sensors. Firstly, we design a confidence inverse normalization guided ghost trajectories suppression module (CIGTS). This module suppresses false detections and ghost trajectories at their source using inverse normalization and a virtual trajectory survival frame strategy. Secondly, an adaptive matching space-driven lightweight association module (AMSLA) is proposed. By discarding global association strategies, this module improves association efficiency and accuracy using low-cost decision factors. Finally, a multi-factor collaborative perception-based intelligent trajectory management module (MFCTM) is constructed. This module enables accurate retention or deletion decisions for unmatched trajectories, thereby reducing computational overhead and the risk of identity mismatches. Extensive experiments on the KITTI dataset show that the proposed method outperforms state-of-the-art methods across multiple performance metrics, achieving Higher Order Tracking Accuracy (HOTA) scores of 80.13% and 53.24% for the Car and Pedestrian categories, respectively.<\/jats:p>","DOI":"10.3390\/s25237351","type":"journal-article","created":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T18:42:02Z","timestamp":1764960122000},"page":"7351","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Lightweight 3D Multi-Object Tracking via Collaborative Camera and LiDAR Sensors"],"prefix":"10.3390","volume":"25","author":[{"given":"Dong","family":"Feng","sequence":"first","affiliation":[{"name":"School of Computer Science, Peking University, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9481-1207","authenticated-orcid":false,"given":"Hengyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Data Science, Qingdao University of Science and Technology, Qingdao 266061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Ocean University of China, Qingdao 266100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Pang, Z., Li, Z., and Wang, N. (2022, January 23\u201327). SimpleTrack: Understanding and Rethinking 3D Multi-Object Tracking. Proceedings of the European Conference on Computer Vision (ECCV), Tel Aviv, Israel.","DOI":"10.1007\/978-3-031-25056-9_43"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8260","DOI":"10.1109\/LRA.2022.3187264","article-title":"DeepFusionMOT: A 3D Multi-Object Tracking Framework Based on Camera-LiDAR Fusion with Deep Association","volume":"7","author":"Wang","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ding, S., Schneider, L., Cordts, M., and Gall, J. (2024, January 17\u201321). ADA-Track: End-to-End Multi-Camera 3D Multi-Object Tracking with Alternating Detection and Association. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01438"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5668","DOI":"10.1109\/TITS.2021.3055616","article-title":"3D Multi-Object Tracking in Point Clouds Based on Prediction Confidence-Guided Data Association","volume":"23","author":"Wu","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Nie, J., Wu, H., He, Z., and Gao, M. (2022). MSA-MOT: Multi-Stage Association for 3D Multimodality Multi-Object Tracking. Sensors, 22.","DOI":"10.3390\/s22228650"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"11981","DOI":"10.1109\/TITS.2023.3285651","article-title":"CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking with Camera-LiDAR Fusion","volume":"24","author":"Wang","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"11241","DOI":"10.1109\/JSEN.2022.3226490","article-title":"StrongFusionMOT: A Multi-Object Tracking Method Based on LiDAR-Camera Fusion","volume":"23","author":"Wang","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kim, H., Lee, H.J., Lee, Y., Lee, J., Kim, H., and Koh, Y.J. (2025, January 11\u201315). GRAE-3DMOT: Geometry Relation-Aware Encoder for Online 3D Multi-Object Tracking. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR52734.2025.01092"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1109\/TITS.2024.3489234","article-title":"MCCA-MOT: Multimodal Collaboration-Guided Cascade Association Network for 3D Multi-Object Tracking","volume":"26","author":"Li","year":"2025","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, P., and Jin, J. (2022, January 19\u201324). Time3D: End-to-End Joint Monocular 3D Object Detection and Tracking for Autonomous Driving. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00386"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Kini, J., Mian, A., and Shah, M. (June, January 29). 3DMODT: Attention-Guided Affinities for Joint Detection & Tracking in 3D Point Clouds. Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA), London, UK.","DOI":"10.1109\/ICRA48891.2023.10160305"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ding, S., Rehder, E., Schneider, L., Cordts, M., and Gall, J. (2023, January 2\u20136). 3DMOTFormer: Graph Transformer for Online 3D Multi-Object Tracking. Proceedings of the 2023 IEEE International Conference on Computer Vision (ICCV), Paris, France.","DOI":"10.1109\/ICCV51070.2023.00897"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zhang, C., Guo, Y., Chen, L., and Happold, M. (2023, January 18\u201322). MotionTrack: End-to-End Transformer-Based Multi-Object Tracking with LiDAR-Camera Fusion. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPRW59228.2023.00020"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Kim, A., O\u0161ep, A., and Leal-Taix\u00e9, L. (June, January 30). EagerMOT: 3D Multi-Object Tracking Via Sensor Fusion. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9562072"},{"key":"ref_15","unstructured":"Wang, X., Fu, C., He, J., Huang, M., Meng, T., Zhang, S., and Zhang, C. (2023). You Only Need Two Detectors to Achieve Multi-Modal 3D Multi-Object Tracking. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"10519","DOI":"10.1109\/LRA.2024.3475882","article-title":"Fast-Poly: A Fast Polyhedral Algorithm for 3D Multi-Object Tracking","volume":"9","author":"Li","year":"2024","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Papais, S., Ren, R., and Waslander, S. (2024, January 13\u201317). SWTrack: Multiple Hypothesis Sliding Window 3D Multi-Object Tracking. Proceedings of the 2024 IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan.","DOI":"10.1109\/ICRA57147.2024.10611067"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"512","DOI":"10.1109\/TIV.2022.3158419","article-title":"3D Multi-Object Tracking with Adaptive Cubature Kalman Filter for Autonomous Driving","volume":"8","author":"Guo","year":"2023","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, K., Liu, Y., Mei, F., Jin, J., and Wang, Y. (2023). Boost Correlation Features with 3D-MiIoU-Based Camera-LiDAR Fusion for MODT in Autonomous Driving. Remote Sens., 15.","DOI":"10.3390\/rs15040874"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Pieroni, R., Specchia, S., Corno, M., and Savaresi, S.M. (2024, January 25\u201328). Multi-Object Tracking with Camera-LiDAR Fusion for Autonomous Driving. Proceedings of the 2024 European Control Conference (ECC), Stockholm, Sweden.","DOI":"10.23919\/ECC64448.2024.10591139"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Nagy, M., Khonji, M., Dias, J., and Javed, S. (June, January 29). DFR-FastMOT: Detection Failure Resistant Tracker for Fast Multi-Object Tracking Based on Sensor Fusion. Proceedings of the 2023 IEEE International Conference on Robotics and Automation (ICRA), London, UK.","DOI":"10.1109\/ICRA48891.2023.10160328"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.sigpro.2023.109367","article-title":"3D Multi-Object Tracking with Boosting Data Association and Improved Trajectory Management Mechanism","volume":"218","author":"Jin","year":"2024","journal-title":"Signal Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"4273","DOI":"10.1109\/LRA.2023.3323124","article-title":"ShaSTA: Modeling Shape and Spatio-Temporal Affinities for 3D Multi-Object Tracking","volume":"9","author":"Sadjadpour","year":"2024","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1326","DOI":"10.1109\/LRA.2023.3342552","article-title":"S.T.A.R.-Track: Latent Motion Models for End-to-End 3D Object Tracking with Adaptive Spatio-Temporal Appearance Representations","volume":"9","author":"Doll","year":"2024","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_25","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 Conference on Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1007\/s11263-020-01375-2","article-title":"HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking","volume":"129","author":"Luiten","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"246309","DOI":"10.1155\/2008\/246309","article-title":"Evaluating Multiple Object Tracking Performance: The Clear MOT Metrics","volume":"2008","author":"Bernardin","year":"2008","journal-title":"Eurasip. J. Image Video Process."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Shi, S., Wang, X., and Li, H. (2019, January 15\u201320). PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00086"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ren, J., Chen, X., Liu, J., Sun, W., Pang, J., Yan, Q., and Xu, L. (2017, January 21\u201326). Accurate Single Stage Detector Using Recurrent Rolling Convolution. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.87"},{"key":"ref_30","unstructured":"Mendon\u00e7a, D., Barros, T., Premebida, C., and Nunes, U.J. (2025). Seg2Track-SAM2: SAM2-based Multi-object Tracking and Segmentation for Zero-shot Generalization. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Claasen, P.J., and de Villiers, J.P. (2024). One Homography is All You Need: IMM-based Joint Homography and Multiple Object State Estimation. arXiv.","DOI":"10.2139\/ssrn.5062122"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1109\/TIV.2023.3286979","article-title":"A Novel Adaptive Noise Covariance Matrix Estimation and Filtering Method: Application to Multiobject Tracking","volume":"9","author":"Jiang","year":"2024","journal-title":"IEEE Trans. Intell. Veh."},{"key":"ref_33","unstructured":"Im, E., Jee, C., and Lee, J.K. (2024). Spb3DTracker: A Robust LiDAR-Based Person Tracker for Noisy Environment. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.sigpro.2024.109544","article-title":"3D Multi-Object Tracking Based on Informatic Divergence-Guided Data Association","volume":"222","author":"He","year":"2024","journal-title":"Signal Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1109\/LRA.2024.3511438","article-title":"A Multi-Modal Fusion-Based 3D Multi-Object Tracking Framework with Joint Detection","volume":"10","author":"Wang","year":"2025","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"21304","DOI":"10.1109\/ACCESS.2024.3356864","article-title":"CollabMOT Stereo Camera Collaborative Multi Object Tracking","volume":"12","author":"Ninh","year":"2024","journal-title":"IEEE Access"},{"key":"ref_37","unstructured":"Yan, Z., Feng, S., Li, X., Zhou, Y., Xia, C., and Li, S. (2025). S3MOT: Monocular 3D Object Tracking with Selective State Space Model. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.patcog.2024.111169","article-title":"Learning Data Association for Multi-Object Tracking Using Only Coordinates","volume":"160","author":"Miah","year":"2025","journal-title":"Pattern Recognit."},{"key":"ref_39","unstructured":"Gong, Y., Chen, M., Liu, H., Gao, Y., Yang, L., Wang, N., Song, Z., and Ma, H. (2025). Stable at Any Speed: Speed-Driven Multi-Object Tracking with Learnable Kalman Filtering. arXiv."},{"key":"ref_40","unstructured":"Tokmakov, P., Jabri, A., Li, J., and Gaidon, A. (2022). Object Permanence Emerges in a Random Walk Along Memory. arXiv."},{"key":"ref_41","unstructured":"Liao, P., Yang, F., Wu, D., Liu, B., and Zhang, X. (2023). MO-YOLO: End-to-End Multiple-Object Tracking Method with YOLO and Decoder. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2044","DOI":"10.1007\/s11263-024-02237-x","article-title":"APPTracker+: Displacement Uncertainty for Occlusion Handling in Low-Frame-Rate Multiple Object Tracking","volume":"133","author":"Zhou","year":"2025","journal-title":"Int. J. Comput. Vis."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"9543","DOI":"10.1109\/JSEN.2022.3155232","article-title":"A Fast and High-Performance Object Proposal Method for Vision Sensors: Application to Object Detection","volume":"22","author":"Jiang","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"8079","DOI":"10.1109\/TII.2024.3366983","article-title":"Rethinking Joint Detection and Embedding for Multiobject Tracking in Multiscenario","volume":"20","author":"Xu","year":"2024","journal-title":"IEEE Trans. Ind. Inform."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/23\/7351\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,5]],"date-time":"2025-12-05T18:49:00Z","timestamp":1764960540000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/23\/7351"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,3]]},"references-count":44,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["s25237351"],"URL":"https:\/\/doi.org\/10.3390\/s25237351","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,3]]}}}