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Open AccessArticle
Intra-Frame Graph Structure and Inter-Frame Bipartite Graph Matching with ReID-Based Occlusion Resilience for Point Cloud Multi-Object Tracking
by
Shaoyu Sun
Shaoyu Sun 1
,
Chunhao Shi
Chunhao Shi 2,
Chunyang Wang
Chunyang Wang 1,*
,
Qing Zhou
Qing Zhou 3,
Rongliang Sun
Rongliang Sun 4,
Bo Xiao
Bo Xiao 3,
Yueyang Ding
Yueyang Ding 1 and
Guan Xi
Guan Xi 3
1
School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130022, China
2
Hong Kong Applied Science and Technology Research Institute, Hong Kong 999077, China
3
Xi’an Key Laboratory of Active Photoelectric Imaging Detection Technology, Xi’an Technological University, Xi’an 710021, China
4
Jinhua Campus, Xi’an University of Technology, Xi’an 710048, China
*
Author to whom correspondence should be addressed.
Electronics 2024, 13(15), 2968; https://doi.org/10.3390/electronics13152968 (registering DOI)
Submission received: 19 June 2024
/
Revised: 22 July 2024
/
Accepted: 25 July 2024
/
Published: 27 July 2024
Abstract
Three-dimensional multi-object tracking (MOT) using lidar point cloud data is crucial for applications in autonomous driving, smart cities, and robotic navigation. It involves identifying objects in point cloud sequence data and consistently assigning unique identities to them throughout the sequence. Occlusions can lead to missed detections, resulting in incorrect data associations and ID switches. To address these challenges, we propose a novel point cloud multi-object tracker called GBRTracker. Our method integrates an intra-frame graph structure into the backbone to extract and aggregate spatial neighborhood node features, significantly reducing detection misses. We construct an inter-frame bipartite graph for data association and design a sophisticated cost matrix based on the center, box size, velocity, and heading angle. Using a minimum-cost flow algorithm to achieve globally optimal matching, thereby reducing ID switches. For unmatched detections, we design a motion-based re-identification (ReID) feature embedding module, which uses velocity and the heading angle to calculate similarity and association probability, reconnecting them with their corresponding trajectory IDs or initializing new tracks. Our method maintains high accuracy and reliability, significantly reducing ID switches and trajectory fragmentation, even in challenging scenarios. We validate the effectiveness of GBRTracker through comparative and ablation experiments on the NuScenes and Waymo Open Datasets, demonstrating its superiority over state-of-the-art methods.
Share and Cite
MDPI and ACS Style
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 2024, 13, 2968.
https://doi.org/10.3390/electronics13152968
AMA Style
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. 2024; 13(15):2968.
https://doi.org/10.3390/electronics13152968
Chicago/Turabian Style
Sun, Shaoyu, Chunhao Shi, Chunyang Wang, Qing Zhou, Rongliang Sun, Bo Xiao, Yueyang Ding, and Guan Xi.
2024. "Intra-Frame Graph Structure and Inter-Frame Bipartite Graph Matching with ReID-Based Occlusion Resilience for Point Cloud Multi-Object Tracking" Electronics 13, no. 15: 2968.
https://doi.org/10.3390/electronics13152968
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