Lightweight and Fast Target Tracking Algorithm Based on Ghost-TiFPN

被引：0

作者：

Yin G. ^{[1
,2
,3
]}

Qi Y. ^{[1
,2
,3
]}

Liu L. ^{[1
,3
]}

Su J. ^{[1
,2
,3
]}

Zhang L. ^{[1
,2
,3
]}

机构：

[1] College of Electric Power, Inner Mongolia University of Technology, Inner Mongolia, Hohhot

[2] Inner Mongolia Key Laboratory of Electromechanical Control, Inner Mongolia, Hohhot

[3] Intelligent Energy Technology and Equipment Engineering Research Centre of Colleges, Universities in the Inner Mongolia Autonomous Region, Inner Mongolia, Hohhot

来源：

Binggong Xuebao/Acta Armamentarii | 2024年 / 45卷 / 05期

关键词：

embedded device; lightweight; siamese network; target tracking;

D O I：

10.12382/bgxb.2022.1272

中图分类号：

学科分类号：

摘要：

In response to the problems that traditional siamese target tracking algorithm is bulky and difficult to deploy in embedded devices, and has poor effect under the conditions of large changes in target scale and similar object interference, a lightweight and fast target tracking algorithm GTtracker is proposed. The Resnet network is redesigned to build a lightweight G-Resnet network by introducing the Ghost mechanism for fast feature extraction of tracked targets. And then the fusion of feature information is further enhanced by designing a lightweight adaptive weighted fusion algorithm TiFPN to solve the problem of similar object interference. After that, a lightweight area regression network GDNet is introduced for target classification, IoU calculation, and bounding box regression, which applies a new target finder in the tracking stage to enhance the success rate of algorithm tracking. Finally, the algorithm is validated on OTB100 dataset and VOT2020 dataset, and ported to Jetson Xavier NX embedded device for performance testing. Experimental results show the effectiveness and superiority of the proposed algorithm, In comparison with classical siamese target tracking algorithm (SiamCAR), the proposed algorithm can achieve faster operation speed and real-time operation on Jetson Xavier NX, reaching 30 frames / s, under the conditions of the same accuracy and EAO metrics, which can effectively solve the problems of similar object interference and large scale variation. © 2024 China Ordnance Industry Corporation. All rights reserved.

引用

页码：1703 / 1716

页数：13

共 21 条

[21]

ZHANG Z P, PENG H W, FU J L, Et al., Ocean:object-aware anchor-free tracking, Proceedings of European Conference on Computer Vision, pp. 771-787, (2020)

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