Aggregated Euclidean Distances for a Fast and Robust Real-Time 3D-MOT

Sadli, Rahmad; Afkir, Mohamed; Hadid, Abdenour; Rivenq, Atika; Taleb-Ahmed, Abdelmalik

Document type :

Article dans une revue scientifique

DOI :

10.1109/JSEN.2021.3104390

Title :

Aggregated Euclidean Distances for a Fast and Robust Real-Time 3D-MOT

Author(s) :

Sadli, Rahmad [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Afkir, Mohamed [Auteur]
Hadid, Abdenour [Auteur]
Institut d’Électronique, de Microélectronique et de Nanotechnologie - UMR 8520 [IEMN]
Rivenq, Atika [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Taleb-Ahmed, Abdelmalik [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]

Journal title :

IEEE Sensors Journal

Pages :

21872-21884

Publisher :

Institute of Electrical and Electronics Engineers

Publication date :

2021

ISSN :

1530-437X

English keyword(s) :

Aggregated Euclidean distances
AED
3D MOT
real-time multi-tracking

HAL domain(s) :

Informatique [cs]/Systèmes embarqués
Sciences de l'ingénieur [physics]

English abstract : [en]

Autonomous driving systems must have the ability to monitor the kinematic behaviour of multiple obstacles. Therefore, 3D multi-object tracking (3D-MOT) is one of the crucial modules in autonomous driving to detect the ...
Show more >Autonomous driving systems must have the ability to monitor the kinematic behaviour of multiple obstacles. Therefore, 3D multi-object tracking (3D-MOT) is one of the crucial modules in autonomous driving to detect the presence of potential hazard movements such as human operated vehicles and pedestrians. In this work, we present a novel online 3D multi-tracking system that uses the Aggregated Euclidean Distances (AED) in data association module instead of using Intersection over Union (IoU) as a new metric. AED is used in order to obtain the relationship between predicted tracks and current object detections. There are several benefits from using AED in data association module. Firstly, it can reduce the system’s complexity so that the execution time can be significantly reduced (as calculating Euclidean distances is much faster than obtaining 3D-IoU). Secondly, AED can provide distance measurement even when there is no overlaps between the predicted tracks and the current detections, while 3D-IoU produces zeros for non-overlapping cases. To demonstrate the validity of our proposed method, we performed extensive experiments on KITTI multi-tracking benchmark and nuScenes validation datasets. The experimental results are compared against the open-sourced state of the art 3D MOTs such as AB3DMOT, FANTrack, and mmMOT. Our method clearly outperforms the AB3DMOT baseline method and other methods in terms of accuracy and/or processing speed.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :