Sökning: onr:"swepub:oai:research.chalmers.se:c7ecafc6-9979-48e8-bc46-bf8ceeb5bfd3" >
GNN-PMB: A Simple b...
GNN-PMB: A Simple but Effective Online 3D Multi-Object Tracker without Bells and Whistles
-
Liu, Jianan (författare)
-
- Bai, Liping (författare)
- Beihang University
-
- Xia, Yuxuan, 1993 (författare)
- Chalmers tekniska högskola,Chalmers University of Technology
-
visa fler...
-
- Huang, Tao (författare)
- James Cook University
-
- Zhu, Bing (författare)
- Beihang University
-
visa färre...
-
(creator_code:org_t)
- 2023
- 2023
- Engelska.
-
Ingår i: IEEE Transactions on Intelligent Vehicles. - 2379-8858. ; 8:2, s. 1176-1189
- Relaterad länk:
-
https://doi.org/10.1...
-
visa fler...
-
https://research.cha...
-
visa färre...
Abstract
Ämnesord
Stäng
- Multi-object tracking (MOT) is among crucial applications in modern advanced driver assistance systems (ADAS) and autonomous driving (AD) systems. The global nearest neighbor (GNN) filter, as the earliest random vector-based Bayesian tracking framework, has been adopted in most of state-of-the-arts trackers in the automotive industry. The development of random finite set (RFS) theory facilitates a mathematically rigorous treatment of the MOT problem, and different variants of RFS-based Bayesian filters have then been proposed. However, their effectiveness in the real ADAS and AD application is still an open problem. In this paper, it is demonstrated that the latest RFS-based Bayesian tracking framework could be superior to typical random vector-based Bayesian tracking framework via a systematic comparative study of both traditional random vector-based Bayesian filters with rule-based heuristic track maintenance and RFS-based Bayesian filters on the nuScenes validation dataset. An RFS-based tracker, namely Poisson multi-Bernoulli filter using the global nearest neighbor (GNN-PMB), is proposed to LiDAR-based MOT tasks. This GNN-PMB tracker is simple to use, and it achieves competitive results on the nuScenes dataset. Specifically, the proposed GNN-PMB tracker outperforms most state-of-the-art LiDAR-only trackers and LiDAR and camera fusion-based trackers, ranking the $3^{rd}$ among all LiDAR-only trackers on nuScenes 3D tracking challenge leader board 1 1 https://bit.ly/3bQJ2CP at the time of submission. Our code is available at https://github.com/chisyliu/GnnPmbTracker .
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Software Engineering (hsv//eng)
- TEKNIK OCH TEKNOLOGIER -- Elektroteknik och elektronik -- Inbäddad systemteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Electrical Engineering, Electronic Engineering, Information Engineering -- Embedded Systems (hsv//eng)
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)
Nyckelord
- Laser radar
- Multi-object tracking
- Radar tracking
- Bayes methods
- Detectors
- GNN-PMB
- random finite set-based Bayesian filters
- Three-dimensional displays
- random vector-based Bayesian filters
- autonomous driving
- Cameras
- LiDAR
- Feature extraction
Publikations- och innehållstyp
- art (ämneskategori)
- ref (ämneskategori)
Hitta via bibliotek
Till lärosätets databas