Pedestrian Tracking Algorithm Combining Contextual Information and Attention Mechanism

Shunliang Xiao; Zanxia Qiang; Weiguang Liu; Xianfu Bao

doi:doi:10.11648/j.ajcst.20210404.14

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Pedestrian Tracking Algorithm Combining Contextual Information and Attention Mechanism

Shunliang Xiao, Zanxia Qiang, Weiguang Liu, Xianfu Bao

Published in American Journal of Computer Science and Technology (Volume 4, Issue 4)

Received: 8 October 2021 Accepted: 25 October 2021 Published: 5 November 2021

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Abstract

In the real scene, because pedestrians are occluded or the size of pedestrians is small, the convolutional neural network cannot fully extract their features, resulting in poor detection results. In two adjacent frames, the same pedestrian is prone to errors when doing data association, which makes the pedestrian tracking effect unsatisfactory. In order to solve this problem, the pedestrian tracking algorithm based on Anchor-free idea is improved. A fusion context information module is proposed to enhance the model's feature extraction ability for different receptive fields, and improve the model's detection and tracking performance when the pedestrian size is small. In addition, in order to let the model learn to pay attention to the effective information of the feature layer. A coordinated attention mechanism is introduced to guide the model to learn the weights of different channels and different regions of the feature layer, and to improve the tracking performance of the model when pedestrians are occluded. In the experiment, the tracking performance of the model was verified on the MOT16 dataset. Experimental results show that compared with other main popular person tracking algorithms, the improved algorithm has higher tracking accuracy and lower pedestrian ID switching times. Its tracking accuracy is 70.74.

Published in	American Journal of Computer Science and Technology (Volume 4, Issue 4)
DOI	10.11648/j.ajcst.20210404.14
Page(s)	111-118
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Pedestrian Tracking, Anchor-Free, Context Information, Attention Mechanism, JDE

References

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APA Style

Shunliang Xiao, Zanxia Qiang, Weiguang Liu, Xianfu Bao. (2021). Pedestrian Tracking Algorithm Combining Contextual Information and Attention Mechanism. American Journal of Computer Science and Technology, 4(4), 111-118. https://doi.org/10.11648/j.ajcst.20210404.14

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ACS Style

Shunliang Xiao; Zanxia Qiang; Weiguang Liu; Xianfu Bao. Pedestrian Tracking Algorithm Combining Contextual Information and Attention Mechanism. Am. J. Comput. Sci. Technol. 2021, 4(4), 111-118. doi: 10.11648/j.ajcst.20210404.14

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AMA Style

Shunliang Xiao, Zanxia Qiang, Weiguang Liu, Xianfu Bao. Pedestrian Tracking Algorithm Combining Contextual Information and Attention Mechanism. Am J Comput Sci Technol. 2021;4(4):111-118. doi: 10.11648/j.ajcst.20210404.14

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@article{10.11648/j.ajcst.20210404.14,
  author = {Shunliang Xiao and Zanxia Qiang and Weiguang Liu and Xianfu Bao},
  title = {Pedestrian Tracking Algorithm Combining Contextual Information and Attention Mechanism},
  journal = {American Journal of Computer Science and Technology},
  volume = {4},
  number = {4},
  pages = {111-118},
  doi = {10.11648/j.ajcst.20210404.14},
  url = {https://doi.org/10.11648/j.ajcst.20210404.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20210404.14},
  abstract = {In the real scene, because pedestrians are occluded or the size of pedestrians is small, the convolutional neural network cannot fully extract their features, resulting in poor detection results. In two adjacent frames, the same pedestrian is prone to errors when doing data association, which makes the pedestrian tracking effect unsatisfactory. In order to solve this problem, the pedestrian tracking algorithm based on Anchor-free idea is improved. A fusion context information module is proposed to enhance the model's feature extraction ability for different receptive fields, and improve the model's detection and tracking performance when the pedestrian size is small. In addition, in order to let the model learn to pay attention to the effective information of the feature layer. A coordinated attention mechanism is introduced to guide the model to learn the weights of different channels and different regions of the feature layer, and to improve the tracking performance of the model when pedestrians are occluded. In the experiment, the tracking performance of the model was verified on the MOT16 dataset. Experimental results show that compared with other main popular person tracking algorithms, the improved algorithm has higher tracking accuracy and lower pedestrian ID switching times. Its tracking accuracy is 70.74.},
 year = {2021}
}

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TY  - JOUR
T1  - Pedestrian Tracking Algorithm Combining Contextual Information and Attention Mechanism
AU  - Shunliang Xiao
AU  - Zanxia Qiang
AU  - Weiguang Liu
AU  - Xianfu Bao
Y1  - 2021/11/05
PY  - 2021
N1  - https://doi.org/10.11648/j.ajcst.20210404.14
DO  - 10.11648/j.ajcst.20210404.14
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 111
EP  - 118
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20210404.14
AB  - In the real scene, because pedestrians are occluded or the size of pedestrians is small, the convolutional neural network cannot fully extract their features, resulting in poor detection results. In two adjacent frames, the same pedestrian is prone to errors when doing data association, which makes the pedestrian tracking effect unsatisfactory. In order to solve this problem, the pedestrian tracking algorithm based on Anchor-free idea is improved. A fusion context information module is proposed to enhance the model's feature extraction ability for different receptive fields, and improve the model's detection and tracking performance when the pedestrian size is small. In addition, in order to let the model learn to pay attention to the effective information of the feature layer. A coordinated attention mechanism is introduced to guide the model to learn the weights of different channels and different regions of the feature layer, and to improve the tracking performance of the model when pedestrians are occluded. In the experiment, the tracking performance of the model was verified on the MOT16 dataset. Experimental results show that compared with other main popular person tracking algorithms, the improved algorithm has higher tracking accuracy and lower pedestrian ID switching times. Its tracking accuracy is 70.74.
VL  - 4
IS  - 4
ER  -

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Author Information

Shunliang Xiao

School of Computer Science, Zhongyuan University of Technology, Zhengzhou, China
Zanxia Qiang

School of Computer Science, Zhongyuan University of Technology, Zhengzhou, China
Weiguang Liu

School of Computer Science, Zhongyuan University of Technology, Zhengzhou, China
Xianfu Bao

School of Computer Science, Zhongyuan University of Technology, Zhengzhou, China

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Plain Text BibTeX RIS

APA Style

Shunliang Xiao, Zanxia Qiang, Weiguang Liu, Xianfu Bao. (2021). Pedestrian Tracking Algorithm Combining Contextual Information and Attention Mechanism. American Journal of Computer Science and Technology, 4(4), 111-118. https://doi.org/10.11648/j.ajcst.20210404.14

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ACS Style

Shunliang Xiao; Zanxia Qiang; Weiguang Liu; Xianfu Bao. Pedestrian Tracking Algorithm Combining Contextual Information and Attention Mechanism. Am. J. Comput. Sci. Technol. 2021, 4(4), 111-118. doi: 10.11648/j.ajcst.20210404.14

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AMA Style

Shunliang Xiao, Zanxia Qiang, Weiguang Liu, Xianfu Bao. Pedestrian Tracking Algorithm Combining Contextual Information and Attention Mechanism. Am J Comput Sci Technol. 2021;4(4):111-118. doi: 10.11648/j.ajcst.20210404.14

Copy | Download

@article{10.11648/j.ajcst.20210404.14,
  author = {Shunliang Xiao and Zanxia Qiang and Weiguang Liu and Xianfu Bao},
  title = {Pedestrian Tracking Algorithm Combining Contextual Information and Attention Mechanism},
  journal = {American Journal of Computer Science and Technology},
  volume = {4},
  number = {4},
  pages = {111-118},
  doi = {10.11648/j.ajcst.20210404.14},
  url = {https://doi.org/10.11648/j.ajcst.20210404.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20210404.14},
  abstract = {In the real scene, because pedestrians are occluded or the size of pedestrians is small, the convolutional neural network cannot fully extract their features, resulting in poor detection results. In two adjacent frames, the same pedestrian is prone to errors when doing data association, which makes the pedestrian tracking effect unsatisfactory. In order to solve this problem, the pedestrian tracking algorithm based on Anchor-free idea is improved. A fusion context information module is proposed to enhance the model's feature extraction ability for different receptive fields, and improve the model's detection and tracking performance when the pedestrian size is small. In addition, in order to let the model learn to pay attention to the effective information of the feature layer. A coordinated attention mechanism is introduced to guide the model to learn the weights of different channels and different regions of the feature layer, and to improve the tracking performance of the model when pedestrians are occluded. In the experiment, the tracking performance of the model was verified on the MOT16 dataset. Experimental results show that compared with other main popular person tracking algorithms, the improved algorithm has higher tracking accuracy and lower pedestrian ID switching times. Its tracking accuracy is 70.74.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Pedestrian Tracking Algorithm Combining Contextual Information and Attention Mechanism
AU  - Shunliang Xiao
AU  - Zanxia Qiang
AU  - Weiguang Liu
AU  - Xianfu Bao
Y1  - 2021/11/05
PY  - 2021
N1  - https://doi.org/10.11648/j.ajcst.20210404.14
DO  - 10.11648/j.ajcst.20210404.14
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 111
EP  - 118
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20210404.14
AB  - In the real scene, because pedestrians are occluded or the size of pedestrians is small, the convolutional neural network cannot fully extract their features, resulting in poor detection results. In two adjacent frames, the same pedestrian is prone to errors when doing data association, which makes the pedestrian tracking effect unsatisfactory. In order to solve this problem, the pedestrian tracking algorithm based on Anchor-free idea is improved. A fusion context information module is proposed to enhance the model's feature extraction ability for different receptive fields, and improve the model's detection and tracking performance when the pedestrian size is small. In addition, in order to let the model learn to pay attention to the effective information of the feature layer. A coordinated attention mechanism is introduced to guide the model to learn the weights of different channels and different regions of the feature layer, and to improve the tracking performance of the model when pedestrians are occluded. In the experiment, the tracking performance of the model was verified on the MOT16 dataset. Experimental results show that compared with other main popular person tracking algorithms, the improved algorithm has higher tracking accuracy and lower pedestrian ID switching times. Its tracking accuracy is 70.74.
VL  - 4
IS  - 4
ER  -

Copy | Download