128 citations · 145 across the 6 of their papers we have counts for
6 papers
Deep LG-Track: An Enhanced Localization-Confidence-Guided Multi-Object Tracker
Ting Meng, Chunyun Fu, Xiangyan Yan +4
Multi-object tracking plays a crucial role in various applications, such as autonomous driving and security surveillance. This study introduces Deep LG-Track, a novel multi-object…
Localization-Guided Track: A Deep Association Multi-Object Tracking Framework Based on Localization Confidence of Detections
Ting Meng, Chunyun Fu, Mingguang Huang +4
In currently available literature, no tracking-by-detection (TBD) paradigm-based tracking method has considered the localization confidence of detection boxes. In most TBD-based me…
NDT-Map-Code: A 3D global descriptor for real-time loop closure detection in lidar SLAM
Lizhou Liao, Wenlei Yan, Li Sun +4
Loop-closure detection, also known as place recognition, aiming to identify previously visited locations, is an essential component of a SLAM system. Existing research on lidar-bas…
You Only Need Two Detectors to Achieve Multi-Modal 3D Multi-Object Tracking
Xiyang Wang, Chunyun Fu, Jiawei He +6
In the classical tracking-by-detection (TBD) paradigm, detection and tracking are separately and sequentially conducted, and data association must be properly performed to achieve…
3D Multi-Object Tracking Based on Uncertainty-Guided Data Association
Jiawei He, Chunyun Fu, Xiyang Wang
In the existing literature, most 3D multi-object tracking algorithms based on the tracking-by-detection framework employed deterministic tracks and detections for similarity calcul…
DeepFusionMOT: A 3D Multi-Object Tracking Framework Based on Camera-LiDAR Fusion with Deep Association
Xiyang Wang, Chunyun Fu, Zhankun Li +2
In the recent literature, on the one hand, many 3D multi-object tracking (MOT) works have focused on tracking accuracy and neglected computation speed, commonly by designing rather…