SpringerLink
Log in

Light field imaging based on a parallel SVM method for recognizing 2D fake pedestrians

  • Published:
Optoelectronics Letters Aims and scope Submit manuscript

Abstract

It is novel to apply three-dimensional (3D) light field imaging technology to recognize two-dimensional (2D) fake pedestrians. In this paper, we propose a parallel support vector machine (SVM) method based on 3D light field imaging (light field camera) and machine learning techniques. A light field (LF) camera with robust sensors, which is able to record rich 3D information, is used as hardware equipment. Histogram of oriented gradient (HOG) feature extraction algorithm and SVM classification method are used to recognize the real and 2D fake pedestrians efficiently. Besides, we carry out an experiment on our improved LF pedestrian dataset. The experimental results of parameter optimization study show that in the case of 400 training samples (200 positive samples and 200 negative samples), 120 to 420 testing samples, and an HOG cellsize as 8×8, the best recognition accuracy with polynomial kernel function is improved by more than 2% compared with the previous method. The best accuracy is 99.17%. Otherwise, the recognition accuracy of more than 98.00% will be obtained even under other experimental conditions.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. BILAL M. Algorithmic optimisation of histogram intersection kernel support vector machine-based pedestrian detection using low complexity features[J]. IET computer vision, 2017, 11(5): 350–357.

    Article  Google Scholar 

  2. LI F, ZHANG R, YOU F. Fast pedestrian detection and dynamic tracking for intelligent vehicles within V2V cooperative environment[J]. IET image processing, 2017, 11(10): 833–840.

    Article  Google Scholar 

  3. BRAUN M, KREBS S, FLOHR F, et al. Eurocity persons: a novel benchmark for person detection in traffic scenes[J]. IEEE transactions on pattern analysis and machine intelligence, 2019, 41(8): 1844–1861.

    Article  Google Scholar 

  4. BILAL M, KHAN A, KHAN M U K, et al. A low-complexity pedestrian detection framework for smart video surveillance systems[J]. IEEE transactions on circuits and systems for video technology, 2016, 27(10): 2260–2273.

    Article  Google Scholar 

  5. LIU S, LI Y F. Precision 3-D motion tracking for binocular microscopic vision system[J]. IEEE transactions on industrial electronics, 2019, 66(12): 9339–9349.

    Article  Google Scholar 

  6. SEPAS-MOGHADDAM A, PEREIRA F, CORREIA P L. Ear recognition in a light field imaging framework: a new perspective[J]. IET biometrics, 2018, 7(3): 224–231.

    Article  Google Scholar 

  7. JEON H G, PARK J, CHOE G, et al. Depth from a light field image with learning-based matching costs[J]. IEEE transactions on pattern analysis and machine intelligence, 2018, 41(2): 297–310.

    Article  Google Scholar 

  8. MIGNARD-DEBISE L, IHRKE I. A vignetting model for light field cameras with an application to light field microscopy[J]. IEEE transactions on computational imaging, 2019, 5(4): 585–595.

    Article  Google Scholar 

  9. ANAND C, JAINWAL K, SARKAR M. A three-phase, one-tap high background light subtraction time-of-flight camera[J]. IEEE transactions on circuits and systems I: regular papers, 2019, 66(6): 2219–2229.

    Article  Google Scholar 

  10. DING Y, ZHAO Y, CHEN X, et al. Stereoscopic image quality assessment by analysing visual hierarchical structures and binocular effects[J]. IET image processing, 2019, 13(10): 1608–1615.

    Article  Google Scholar 

  11. LFP (light field photography) file reader[EB/OL]. (2014)[2021-05-20]. http://code.behnam.es/pythonlfp-reader.

  12. JIA C, SHI F, ZHAO Y, et al. Identification of pedestrians from confused planar objects using light field imaging[J]. IEEE access, 2018, 6: 39375–39384.

    Article  Google Scholar 

  13. DENG F, GUO S, ZHOU R, et al. Sensor multifault diagnosis with improved support vector machines[J]. IEEE transactions on automation science and engineering, 2015, 14(2): 1053–1063.

    Article  Google Scholar 

  14. RAGHAVENDRA R, RAJA K B, BUSCH C. Presentation attack detection for face recognition using light field camera[J]. IEEE transactions on image processing, 2015, 24(3): 1060–1075.

    Article  MathSciNet  ADS  Google Scholar 

  15. Lytro Inc[EB/OL]. (2014-06-02)[2021-05-20]. http://www.lytro.com/.

Download references

Author information

Authors and Affiliations

  1. Key Laboratory of Computer Vision and System of Ministry of Education, Tian** Key Laboratory of Intelligence Computing and Novel Software Technology, Tian** University of Technology, Tian**, 300384, China

    Chen Jia, Yao Zhang, Fan Shi & Meng Zhao

  2. The MOE Key Laboratory of Weak-Light Nonlinear Photonics, Nankai University, Tian**, 300457, China

    Fan Shi

Authors
  1. Chen Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fan Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Meng Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yao Zhang or Fan Shi.

Additional information

Statements and Declarations

The authors declare that there are no conflicts of interest related to this article.

This work has been supported by the National Natural Science Foundation of China (Nos. 61906133, 62020106004, 92048301 and 61703304).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jia, C., Zhang, Y., Shi, F. et al. Light field imaging based on a parallel SVM method for recognizing 2D fake pedestrians. Optoelectron. Lett. 18, 48–53 (2022). https://doi.org/10.1007/s11801-022-1047-4

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11801-022-1047-4

Document code

Search

Navigation