Qiancheng Bei
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Qiancheng Bei has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Haiying Liu
School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, China
1 shared publication - Lixia Deng
School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
1 shared publication - Yuncheng Pei
School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China
1 shared publication
Latest Publications
- Open Access
- 13 June 2022
- Published by MDPI AG in Applied System Innovation
- Vol. 5 (3) , 55
Improved DeepSORT Algorithm Based on Multi-Feature Fusion
At present, the detection-based pedestrian multi-target tracking algorithm is widely used in artificial intelligence, unmanned driving cars, virtual reality and other fields, and has achieved good tracking results. The traditional DeepSORT algorithm mainly tracks multiple pedestrian targets continuously, and can keep the ID unchanged. The applicability and tracking accuracy of the algorithm need to be further improved during tracking. In order to improve the tracking accuracy of the DeepSORT method, we propose a novel algorithm by revising the IOU distance metric in the matching process and integrating Feature Pyramid Network (FPN) and multi-layer pedestrian appearance features. The improved algorithm is verified on the public MOT-16 dataset, and the tracking accuracy of the algorithm is improved by 4.1%.
- 13 June 2022
- Published by MDPI AG in Applied System Innovation
- Vol. 5 (3) , 55
Open AccessImproved DeepSORT Algorithm Based on Multi-Feature Fusion
At present, the detection-based pedestrian multi-target tracking algorithm is widely used in artificial intelligence, unmanned driving cars, virtual reality and other fields, and has achieved good tracking results. The traditional DeepSORT algorithm mainly tracks multiple pedestrian targets continuously, and can keep the ID unchanged. The applicability and tracking accuracy of the algorithm need to be further improved during tracking. In order to improve the tracking accuracy of the DeepSORT method, we propose a novel algorithm by revising the IOU distance metric in the matching process and integrating Feature Pyramid Network (FPN) and multi-layer pedestrian appearance features. The improved algorithm is verified on the public MOT-16 dataset, and the tracking accuracy of the algorithm is improved by 4.1%.