Smart City Gnosys
Smart city article details
| Title | Deep Learning-Based 3D Multi-Object Tracking Using Multimodal Fusion In Smart Cities |
|---|---|
| ID_Doc | 17929 |
| Authors | Li H.; Liu X.; Jia H.; Ahanger T.A.; Xu L.; Alzamil Z.; Li X. |
| Year | 2024 |
| Published | Human-centric Computing and Information Sciences, 14 |
| DOI | http://dx.doi.org/10.22967/HCIS.2024.14.047 |
| Abstract | The intelligent processing of visual perception information is one of the core technologies of smart cities. Deep learning-based 3D multi-object tracking is important in improving the intelligence and safety of robots in smart cities. However, 3D multi-object tracking still faces many challenges due to the complexity of the environment and uncertainty of the object. In this paper, we make the most of the multimodal information of image and point cloud and propose a multimodal adaptive feature gating fusion module to improve the feature fusion effect. In the object association stage, we designed an orientation-position-aware affinity matrix (EO-IoU) by using Euclidean distance, orientation similarity, and intersection over union, which is more suitable for the association to solve the problem of association failure when there is little or no overlap between the detection box and the prediction box. At the same time, we adopt a more robust two-stage data association method to solve the trajectory fragmentation and identity switching caused by discarding low-scoring detection boxes. The results of extensive experiments on the KITTI and NuScenes benchmark datasets demonstrate that our method outperforms existing state-of-the-art methods with better robustness and accuracy. © (2024), (Korea Information Processing Society). All rights reserved. |
| Author Keywords | 3D Multi-Object Tracking; Data Association; Multimodal Feature Fusion; Position Affinity Matrix; Smart Cities; Visual Perception |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
17814  | 0.884 | Wang X.; Sun Z.; Chehri A.; Jeon G.; Song Y. | Deep Learning And Multi-Modal Fusion For Real-Time Multi-Object Tracking: Algorithms, Challenges, Datasets, And Comparative Study | Information Fusion, 105 (2024) |
| 38289 | 0.869 | Zhang W.; Xin Y.; Zheng C.; Peng X.; Tai J. | Multi-Object Tracking Algorithm Based On Multi-Layer Feature Adaptive Fusion | Proceedings - 2022 2nd International Conference on Frontiers of Electronics, Information and Computation Technologies, ICFEICT 2022 (2022) |
| 38265 | 0.856 | Luo Y.; Wang T.; Lu S.; Dai X.; Li Z. | Multi-Lidar-Based 3D Object Detection Via Data-Level Fusion Method | International Journal of Antennas and Propagation, 2025, 1 (2025) |
| 38290 | 0.854 | Zhou X.; Jia Y.; Bai C.; Zhu H.; Chan S. | Multi-Object Tracking Based On Attention Networks For Smart City System | Sustainable Energy Technologies and Assessments, 52 (2022) |
| 33198 | 0.854 | Sun S.; Shi C.; Wang C.; Zhou Q.; Sun R.; Xiao B.; Ding Y.; Xi G. | Intra-Frame Graph Structure And Inter-Frame Bipartite Graph Matching With Reid-Based Occlusion Resilience For Point Cloud Multi-Object Tracking | Electronics (Switzerland), 13, 15 (2024) |
| 38397 | 0.851 | Wang X.; Li K.; Chehri A. | Multi-Sensor Fusion Technology For 3D Object Detection In Autonomous Driving: A Review | IEEE Transactions on Intelligent Transportation Systems, 25, 2 (2024) |
| 23879 | 0.851 | Syamal S.; Huang C.; Petrunin I. | Enhancing Object Detection And Localization Through Multi-Sensor Fusion For Smart City Infrastructure | 2024 IEEE International Workshop on Metrology for Automotive, MetroAutomotive 2024 - Proceedings (2024) |