Improved DeepSORT Algorithm Based on Multi-Feature Fusion

Applied System Innovation

卷:5

Haiying Liu¹  Lixia Deng¹  Yuncheng Pei¹  Qiancheng Bei¹ 

[1] School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China;

关键词: multi-target tracking;    DeepSORT;    feature extraction;    target detection;   

DOI  :  10.3390/asi5030055

来源: DOAJ

【 摘 要 】

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%.

【 授权许可】

Unknown