Online Multiple Object Tracking Using Min-Cost Flow on Temporal Window for Autonomous Driving
-
Published:2023-09-02
Issue:9
Volume:14
Page:243
-
ISSN:2032-6653
-
Container-title:World Electric Vehicle Journal
-
language:en
-
Short-container-title:WEVJ
Author:
Wei Hongjian12ORCID, Huang Yingping2, Zhang Qian3, Guo Zhiyang4
Affiliation:
1. School of Physics and Electronic Engineering, Fuyang Normal University, Fuyang 236037, China 2. School of Optical-Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 3. School of Information Technology, Jiangsu Open University, Nanjing 210036, China 4. School of Traffic Engineering, Jiangsu Shipping College, Nantong 226010, China
Abstract
Multiple object tracking (MOT), as a core technology for environment perception in autonomous driving, has attracted attention from researchers. Combing the advantages of batch global optimization, we present a novel online MOT framework for autonomous driving, consisting of feature extraction and data association on a temporal window. In the feature extraction stage, we design a three-channel appearance feature extraction network based on metric learning by using ResNet50 as the backbone network and the triplet loss function and employ a Kalman Filter with a constant acceleration motion model to optimize and predict the object bounding box information, so as to obtain reliable and discriminative object representation features. For data association, to reduce the ID switches, the min-cost flow of global association is introduced within the temporal window composed of consecutive multi-frame images. The trajectories within the temporal window are divided into two categories, active trajectories and inactive trajectories, and the appearance, motion affinities between each category of trajectories, and detections are calculated, respectively. Based on this, a sparse affinity network is constructed, and the data association is achieved using the min-cost flow problem of the network. Qualitative experimental results on KITTI MOT public benchmark dataset and real-world campus scenario sequences validate the effectiveness and robustness of our method. Compared with the homogeneous, vision-based MOT methods, quantitative experimental results demonstrate that our method has competitive advantages in terms of higher order tracking accuracy, association accuracy, and ID switches.
Funder
Shanghai Nature Science Foundation of Shanghai Science and Technology Commission, China National Natural Science Foundation of China
Subject
Automotive Engineering
Reference40 articles.
1. 3D multi-object tracking with adaptive cubature kalman filter for autonomous driving;Guo;IEEE Trans. Intell. Veh.,2023 2. Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., and Wang, X. (2022, January 23–27). Bytetrack: Multi-object tracking by associating every detection box. Proceedings of the European Conference on Computer Vision (ECCV), Tel Aviv, Israel. 3. Online multiple object tracking using joint detection and embedding network;Chan;Pattern Recognit.,2022 4. Abudayyeh, D., Almomani, M., Almomani, O., Alsoud, H., and Alsalman, F. (2023). Perceptions of autonomous vehicles: A case study of Jordan. World Electr. Veh. J., 14. 5. Alqarqaz, M., Bani Younes, M., and Qaddoura, R. (2023). An Object Classification Approach for Autonomous Vehicles Using Machine Learning Techniques. World Electr. Veh. J., 14.
|
|