DEVELOPING A COMPUTER VISION RE-IDENTIFICATION SYSTEM

Authors

  • Maksym Ostapenko master Institute of Telecommunication Systems, Igor Sikorsky Kyiv Polytechnic Institute Kyiv, Ukraine,
  • Olena Shtogrina associate professor Institute of Telecommunication Systems, Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, Ukraine,
  • Larysa Globa Prof DSc. in Computer Engineering, IEEE Professional Member, Chair of Information-telecommunication Networks, Institute of Telecommunication Systems, National Technical University of Ukraine "Igor Sikorsky Kiev Polytechnic Institute", Kyiv, Ukraine,
  • Andrii Astrakhantsev associate professor Institute of Telecommunication Systems, Igor Sikorsky Kyiv Polytechnic Institute, Kyiv, Ukraine,
  • Eduard Siemens professor Anhalt University of Applied Sciences, Future Internet Lab Anhalt, Köthen, Germany,

DOI:

https://doi.org/10.20535/2411-2976.12020.35-40

Keywords:

machine learning, video surveillance, re-identification, computer vision, deep learning

Abstract

Background. The rapid growth of computational power of machines and amount of data caused exploding usage of computer vision in a large variety of tasks and in particular for people recognition.
Objective. The aim of the paper is to propose a computer vision re-identification system based on research. Also improvements for detection and recognition models of the system are made.
Methods. We used classical computer vision and deep learning techniques to create the system.
Results. The main contribution of the research is a description of the optimal system structure with a trade-of between speed and quality. Furthermore, requirements for an environment are proposed, which allows to set up the system in the real world with guaranteed quality.
Conclusions. Real-time computer vision re-identification system was developed and can be used in a production environment which satisfy requirements.

References

Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun

“Deep 1Residual Learning for Image Recognition”.

arXiv:1512.03385, 2015.

Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry

Kalenichenko, Weijun Wang, Tobias Weyand, Marco

Andreetto, Hartwig Adam “MobileNets: Efficient

Convolutional Neural Networks for Mobile Vision

Applications”. arXiv:1704.04861, 2017.

Joseph Redmon, Santosh Divvala, Ross Girshick, Ali

Farhadi “You Only Look Once: Unified, Real-Time Object

Detection”. arXiv:1506.02640, 2015.

Wei Liu, Dragomir Anguelov, Dumitru Erhan,

Christian Szegedy, Scott Reed, Cheng-Yang Fu, Alexander C.

Berg “SSD: Single Shot MultiBox Detector”.

arXiv:1512.02325, 2016.

E. Bochinski, V. Eiselein, T. Sikora. High-Speed

Tracking-by-Detection Without Using Image Information. In

International Workshop on Traffic and Street Surveillance for

Safety and Security at IEEE AVSS 2017, 2017.

Hamed Kiani Galoogahi, Ashton Fagg, Simon Lucey

“Learning Background-Aware Correlation Filters for Visual

Tracking”. arXiv:1703.04590, 2017.

Yandong Wen, Kaipeng Zhang, Zhifeng Li and Yu

Qiao. A Discriminative Feature Learning Approach for Deep

Face Recognition. The Chinese University of Hong Kong, Sha

Tin, Hong Kong, 2016.

Jiankang Deng, Jia Guo, Niannan Xue, Stefanos

Zafeiriou “ArcFace: Additive Angular Margin Loss for Deep

Face Recognition” arXiv:1801.07698, 2018.

Yu. A. Malkov, D. A. Yashunin “Efficient and robust

approximate nearest neighbour search using Hierarchical

Navigable Small World graphs”. arXiv:1603.09320, 2016.

Sanghyun Woo, Jongchan Park, Joon-Young Lee, In

So Kweon “CBAM: Convolutional Block Attention Module”.

arXiv:1807.06521, 2018.

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Published

2020-06-28

Issue

No. 1 (2020)

Section

Статті