[go: up one dir, main page]

Skip to main content

Human Detection Algorithm Based on Edge Symmetry

  • Conference paper
Robot Intelligence Technology and Applications 3

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 345))

Abstract

One of the most important abilities that personal robots need when interacting with humans is the ability to detecte human timely. Due to the scan of the input image without any disparity in the traditional method for human detection, processing speed can not meet the demand of a real-time system appropriately. Under such a circumstance, an edge symmetry based human detection algorithm is proposed. With mechanism of scan lines, the symmetrical value of each pixel is calculated along scan line and candidate regions are picked out. Then candidate regions are verified by using Histograms of Oriented Gradients (HOG) feature and Support Vector Machine(SVM) classifier. Experiment shows that the algorithm has a good command of keeping the precise of the recognition as well as elevating the speed of calculation.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 886–893 (2005)

    Google Scholar 

  2. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: IEEE Computer Vision and Pattern Recognition, vol. 1, pp. I-511–I-518 (2001)

    Google Scholar 

  3. Paisitkriangkrai, S., Shen, C., Zhang, J.: Fast pedestrian detection using a cascade of boosted covariance features. IEEE Circuits and Systems for Video Technology 18(8), 1140–1151 (2008)

    Article  Google Scholar 

  4. Arie, M., Moro, A., Hoshikawa, Y.: Fast and stable human detection using multiple classifiers based on subtraction stereo with HOG features. In: IEEE Robotics and Automation (ICRA), pp. 868–873 (2011)

    Google Scholar 

  5. Bertozzi, M., Broggi, A., Rose, M.D.: A symmetry-based validator and refinement system for pedestrian detection in far infrared images. In: IEEE Intelligent Transportation Systems Conference, pp. 155–160 (2007)

    Google Scholar 

  6. Xia, L., Chen, C.C., Aggarwal, J.K.: Human detection using depth information by kinect. In: IEEE Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 15–22 (2011)

    Google Scholar 

  7. Schauland, S., Kummert, A., Park, S.B., Urgel, U.I., Zhang, Y.: Vision-based pedestrian detection-improvement and verification offeature extraction methods and svm-based classification. In: IEEE Intelligent Transportation Systems Conference, pp. 97–102 (2006)

    Google Scholar 

  8. Cosma, A.C., Brehar, R., Nedevschi, S.: Part-based pedestrian detection using HoG features and vertical symmetry. In: IEEE Intelligent Computer Communication and Processing (ICCP), pp. 229–236 (2012)

    Google Scholar 

  9. Treder, M.S.: Behind the looking-glass: A review on human symmetry perception. Symmetry 2(3), 1510–1543 (2010)

    Article  Google Scholar 

  10. Teoh, S.S., Bräunl, T.: Symmetry-based monocular vehicle detection system. Machine Vision and Applications 23(5), 831–842 (2012)

    Article  Google Scholar 

  11. Ma, S.D., Zhang, Z.Z.: Computer vision: the computational theory and algorithm foundation, p. 53. Science Press (1998)

    Google Scholar 

  12. Canny, J.: A computational approach to edge detection. IEEE Pattern Analysis and Machine Intelligence (6), 679–698 (1986)

    Google Scholar 

  13. Neubeck, A., Van, L.G.: Efficient non-maximum suppression. In: IEEE International Conference on Pattern Recognition, vol. 3, pp. 850–855 (2006)

    Google Scholar 

  14. Vapnik, V.: The nature of statistical learning theory. Springer (2000)

    Google Scholar 

  15. Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology (TIST) 2(3), 27 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Wang, H., Chen, J., Fang, B., Dai, S. (2015). Human Detection Algorithm Based on Edge Symmetry. In: Kim, JH., Yang, W., Jo, J., Sincak, P., Myung, H. (eds) Robot Intelligence Technology and Applications 3. Advances in Intelligent Systems and Computing, vol 345. Springer, Cham. https://doi.org/10.1007/978-3-319-16841-8_65

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-16841-8_65

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16840-1

  • Online ISBN: 978-3-319-16841-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics