Article
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Preserved in Portico This version is not peer-reviewed
Improved DeepSORT Algorithm Based on Multi Feature Fusion
Version 1
: Received: 14 April 2022 / Approved: 27 April 2022 / Online: 27 April 2022 (09:01:45 CEST)
A peer-reviewed article of this Preprint also exists.
Liu, H.; Pei, Y.; Bei, Q.; Deng, L. Improved DeepSORT Algorithm Based on Multi-Feature Fusion. Appl. Syst. Innov. 2022, 5, 55. Liu, H.; Pei, Y.; Bei, Q.; Deng, L. Improved DeepSORT Algorithm Based on Multi-Feature Fusion. Appl. Syst. Innov. 2022, 5, 55.
Abstract
Pedestrian multi-target tracking technology plays an important role in artificial intelligence, driverless, virtual reality and other fields. The pedestrian multi-target tracking algorithm DeepSORT based on detection is widely used in industry. It mainly tracks multiple pedestrian targets continuously and keeps their ID unchanged. In order to improve the applicability and tracking accuracy of DeepSORT algorithm, this paper improved the IOU distance measurement in the matching process. At the same time, ResNet50 is used as the feature extraction backbone network, and combined with FPN (Feature Pyramid Network), the appearance features of multi-layer pedestrians are fused to improve the tracking accuracy of DeepSORT algorithm. The proposed algorithm is verified on the public data set MOT-16 and it’s tracking accuracy is enhanced to 4.1%.
Keywords
multi-target tracking; DeepSORT; feature extraction; target detection
Subject
Engineering, Electrical and Electronic Engineering
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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