Skip to main content
SpringerLink
Log in

Optimization of the Computer Vision System for the Detection of Moving Objects

  • Conference paper
  • First Online:
Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges (ICPR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13644))

Included in the following conference series:

Abstract

The main goal of the presented work is to optimize the developed intelligent system for recognizing and detecting vehicles on video data using the YOLOv3 convolutional neural network. Basic results are obtained for real work conditions with the use of graphic processors. In addition, a special performance study was made for the Intel Core i5–8500 CPU. Optimization is based not only on classical neural network methods, such as model pruning, but also modified procedures have been proposed for efficient processing of video information, in particular, optical flow and motion prediction. After the optimization, the data processing speed increased by 4 times when using the NVIDIA RTX 2080 Super GPU and amounted to about 30 frames per second. CPU acceleration was achieved using the Intel OpenVINO toolkit. Performance on the CPU reached almost the same values as on the video card, and the acceleration was almost 30 times from slowest model to fastest. It is important to note that the implementation of optical flow and motion extrapolation was not required on the CPU.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Andriyanov, N., Dementiev, V., Kondratiev, D.: Tracking of Objects in Video Sequences. In: Czarnowski, I., Howlett, R.J., Jain, L.C. (eds.) Intelligent Decision Technologies. SIST, vol. 238, pp. 253–262. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-2765-1_21

    Chapter  Google Scholar 

  2. Li, G., Zhang, D., Zeng, J., Chen, S.: Vehicle monitor system for public transport management based on embedded technology. Phys. Procedia 24(2), 953–960 (2012). https://doi.org/10.1016/j.phpro.2012.02.143

    Article  Google Scholar 

  3. Khan, S.U., Alam, N., Jan, S.U., Koo, I.S.: IoT-Enabled vehicle speed monitoring system. Electronics 11, 614 (2022). https://doi.org/10.3390/electronics11040614

    Article  Google Scholar 

  4. Cai, Z., Vasconcelos N.: Cascade R-CNN: High Quality Object Detection and Instance Segmentation, https://arxiv.org/abs/1906.09756, Accessed 27 Aug 2022

  5. Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-End Object Detection with Transformers, https://arxiv.org/abs/2005.12872, Accessed 27 Aug 2022

  6. Song, X., Gu, W.: Multi-objective real-time vehicle detection method based on yolov5. In: 2021 International Symposium on Artificial Intelligence and its Application on Media (ISAIAM) Proceedings, pp. 142–145 (2021). https://doi.org/10.1109/ISAIAM53259.2021.00037

  7. Andriyanov, N.: Analysis of the acceleration of neural networks inference on Intel processors based on OpenVINO Toolkit. In: Proceedings of 2020 Systems of Signal Synchronization, Generating and Processing in Telecommunications, SYNCHROINFO, pp. 1–6 (2020). https://doi.org/10.1109/SYNCHROINFO49631.2020.9166067

  8. Arnautović, A., Teskeredzic, E.: Evaluation of artificial neural network inference speed and energy consumption on embedded systems. INFOTEH 1, 1–5 (2021). https://doi.org/10.1109/INFOTEH51037.2021.9400658

    Article  Google Scholar 

  9. Zhang, X., Zou, J., He, K., Sun, J.: Accelerating very deep convolutional networks for classification and detection, https://arxiv.org/pdf/1505.06798.pdf, Accessed 28 Aug 2022

  10. Xiao, B., Shi, W., Lu, G.: An optimized quantization technique for image compression using discrete tchebichef transform. Pattern Recognit. Image Anal. 28, 371–378 (2018). https://doi.org/10.1134/S1054661818030021

    Article  Google Scholar 

  11. Novac, P.E., Boukli, G.H., Pegatoquet, A., Miramond, B., Gripon, V.: Quantization and deployment of deep neural networks on microcontrollers. Sensors 21, 2984 (2021). https://doi.org/10.3390/s21092984

    Article  Google Scholar 

  12. Shirokanev, A.S., Andriyanov, N.A., Ilyasova, N.Y.: Development of vector algorithm using CUDA technology for three-dimensional retinal laser coagulation process modeling. Comput. Opt. 45(3), 427–437 (2021). https://doi.org/10.18287/2412-6179-CO-828

    Article  Google Scholar 

  13. Andriyanov, N.A., Vasil'ev, K.K.; Dement'ev, V.E.: Investigation of filtering and objects detection algorithms for a multizone image sequence. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 42, is. 2/W12, 7–10 (2019). https://doi.org/10.5194/isprs-archives-XLII-2-W12-7-2019

  14. Borman, S., Stevenson, R.: Image Sequence Processing. University of Notre Dame, Paris (2002)

    Google Scholar 

  15. Anitha, E., Jiji, C.V.: Optical acceleration for motion description in videos. In: CVPR 2017 Proceedings, pp. 1–9 (2017)

    Google Scholar 

  16. Flynn, J., Neulander, I., Philbin, J., Snavely, N.: Deepstereo: Learning to predict new views from the world’s imagery. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5515–5524 (2016)

    Google Scholar 

  17. Andriyanov, N.A., Dementiev, V.E., Tashlinskii, A.G.: Detection of objects in the images: from likelihood relationships towards scalable and efficient neural networks. Comput. Opt. 46(1), 139–159 (2022). https://doi.org/10.18287/2412-6179-CO-922

    Article  Google Scholar 

  18. Girshick, R.: Fast R-CNN. In: Proceedings of International Conference on Computer Vision (ICCV), vol. 1, pp. 1440–1448 (2015). https://doi.org/10.1109/ICCV.2015.169

  19. Redmon, J., Farhadi, A.: YOLOv3: An incremental improvement, https://arxiv.org/abs/1804.02767 Accessed 30 Aug 2022

  20. Dementyiev, V.E., Andriyanov, N.A., Vasilyiev, K.K.: Use of images augmentation and implementation of doubly stochastic models for improving accuracy of recognition algorithms based on convolutional neural networks. In: Proceedings of 2020 Systems of Signal Synchronization, Generating and Processing in Telecommunications, SYNCHROINFO, pp. 1–4 (2020). https://doi.org/10.1109/SYNCHROINFO49631.2020.9166000

  21. Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11, 125 (2020). https://doi.org/10.3390/info11020125

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Financial University Under the Government of the Russian Federation, Leningradsky Pr-T 49/2 125167, Moscow, Russian Federation

    Nikita Andriyanov

  2. Ulyanovsk State Technical University, Ul. Severny Venets, 32 432027, Ulyanovsk, Russian Federation

    Vitaly Dementiev & Alexandr Tashlinskiy

Authors
  1. Nikita Andriyanov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vitaly Dementiev
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexandr Tashlinskiy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nikita Andriyanov .

Editor information

Editors and Affiliations

  1. York University, Toronto, ON, Canada

    Jean-Jacques Rousseau

  2. Ontario Tech University, Oshawa, ON, Canada

    Bill Kapralos

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Andriyanov, N., Dementiev, V., Tashlinskiy, A. (2023). Optimization of the Computer Vision System for the Detection of Moving Objects. In: Rousseau, JJ., Kapralos, B. (eds) Pattern Recognition, Computer Vision, and Image Processing. ICPR 2022 International Workshops and Challenges. ICPR 2022. Lecture Notes in Computer Science, vol 13644. Springer, Cham. https://doi.org/10.1007/978-3-031-37742-6_32

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-37742-6_32

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37741-9

  • Online ISBN: 978-3-031-37742-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation