Skip to main content
SpringerLink
Log in

Pedestrian Detection - A Survey

  • Conference paper
  • First Online:
Intelligent Computing Paradigm and Cutting-edge Technologies (ICICCT 2019)

Part of the book series: Learning and Analytics in Intelligent Systems ((LAIS,volume 9))

Abstract

Pedestrian detection is one of the important fields of computer vision. Computer vision is getting more information from the digital images and videos and distinguishes between objects and classifies the object. Pedestrian detection is being applied in a wide range of applications such as video surveillance, automated driving, etc. This pedestrian detection has been a growing research area and then so many techniques have been used for detection. This paper focuses on various techniques applied to pedestrian detection and discusses the outcome of every technique and its accuracy and miss rate.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and 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
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover 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. Dalal, N., Triggs, B.: Histogram of oriented gradients for human detection. In: 2005 IEEE International Conference on Computer Vision and Pattern Recognition, CVPR 2005, pp. 886–893 (2005). Print ISSN 1063-6919

    Google Scholar 

  2. You, M., Zhang, Y., Shen, C., Zhang, X.: An extended filtered channel framework for pedestrian detection. IEEE Trans. Intell. Transp. Syst. 19(5), 1640–1651 (2018)

    Article  Google Scholar 

  3. Li, F., Zhang, R., You, F.: Fast pedestrian detection and dynamic tracking for intelligent vehicles within V2V cooperative environment. IET Image Process. 11(10), 833–840 (2017)

    Article  Google Scholar 

  4. Zhang, X., Cheng, L., Li, B., Hu, H.M.: Too far to see? Not really:- pedestrian detection with scale-aware localization policy. IEEE Trans. Image Process. 27(8), 3703–3715 (2018)

    Article  MathSciNet  Google Scholar 

  5. Cao, J., Pang, Y., Li, X.: Learning multilayer channel features for pedestrian detection. IEEE Trans. Image Process. 26(7), 3210–3220 (2017)

    Article  MathSciNet  Google Scholar 

  6. Cao, J., Pang, Y., Li, X.: Pedestrian detection inspired by appearance constancy and shape symmetry. IEEE Trans. Image Process. 25(12), 5538–5551 (2016)

    Article  MathSciNet  Google Scholar 

  7. Zhu, C., Peng, Y.: A boosted multi-task model for pedestrian detection with occlusion handling. IEEE Trans. Image Process. 24(12), 5619–5629 (2015)

    Article  MathSciNet  Google Scholar 

  8. Chen, Y., Xie, H., Shin, H.: Multi-layer fusion techniques using a CNN for multispectral pedestrian detection. IET Comput. Vis. 12(8), 1179–1187 (2018)

    Article  Google Scholar 

  9. Li, Q., Shao, C.F., Zhao, Y.: A robust system for real-time pedestrian detection and tracking. J. Cent. South Univ. 21(4), 1643–1653 (2014)

    Article  Google Scholar 

  10. Choudhury, S.K., Sa, P.K., Prasad Padhy, R., Sharma, S., Bakshi, S.: Improved pedestrian detection using motion segmentation and silhouette orientation. Multimed. Tools Appl. 77(11), 13075–13114 (2018)

    Article  Google Scholar 

  11. Varga, D., Szirányi, T.: Robust real-time pedestrian detection in surveillance videos. J. Ambient Intell. Humaniz. Comput. 8(1), 79–85 (2017)

    Article  Google Scholar 

  12. Bilal, M., Khan, A., Khan, M.U.K., Kyung, C.M.: A low-complexity pedestrian detection framework for smart video surveillance systems. IEEE Trans. Circuits Syst. Video Technol. 27(10), 2260–2273 (2017)

    Article  Google Scholar 

  13. Li, J., Liang, X., Shen, S., Xu, T., Feng, J., Yan, S.: Scale-aware fast R-CNN for pedestrian detection. IEEE Trans. Multimed. 20(4), 985–996 (2018)

    Google Scholar 

  14. Xu, Y., Zhou, X., Liu, P., Xu, H.: Rapid pedestrian detection based on deep omega-shape features with partial occlusion handing. Neural Process. Lett. 49, 1–15 (2018)

    Google Scholar 

  15. Yoshihashi, R., Trinh, T.T., Kawakami, R., You, S., Iida, M., Naemura, T.: Pedestrian detection with motion features via two-stream ConvNets. IPSJ Trans. Comput. Vis. Appl. 10(1), 12 (2018)

    Article  Google Scholar 

  16. Ouyang, W., Zeng, X., Wang, X.: Partial occlusion handling in pedestrian detection with a deep model. IEEE Trans. Circuits Syst. Video Technol. 26(11), 2123–2137 (2016)

    Article  Google Scholar 

  17. Tsimhoni, O., Flannagan, M.: Pedestrian detection with night vision systems enhanced by automatic warnings. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 50, no. 22, pp. 2443–2447 (2012)

    Article  Google Scholar 

  18. Pawlowski, P., Piniarski, K., Dabrowski, A.: Pedestrian detection in low resolution night vision images. In: Signal Processing: Algorithms, Architectures, Arrangements, and Applications (SPA), vol. 2015-Decem, pp. 185–190 (2015)

    Google Scholar 

  19. Ge, J., Luo, Y., Tei, G.: Real-time pedestrian detection and tracking at nighttime for driver-assistance systems. IEEE Trans. Intell. Transp. Syst. 10(2), 283–298 (2009)

    Article  Google Scholar 

  20. Sidla, O., Rosner, M.: HOG pedestrian detection applied to scenes with heavy occlusion. In: Intelligent Robots and Computer Vision XXV: Algorithms, Techniques, and Active Vision, vol. 6764, pp. 676408 (2007)

    Google Scholar 

  21. Wang, L., Xu, L., Yang, M.H.: Pedestrian detection in crowded scenes via scale and occlusion analysis. In: Proceedings of the International Conference on Image Processing, ICIP, vol. 2016-Augus, pp. 1210–1214 (2016)

    Google Scholar 

  22. Wang, B., Chan, K.L., Wang, G., Zhang, H.: Pedestrian detection in highly crowded scenes using ‘online’ dictionary learning for occlusion handling. In: 2014 IEEE International Conference on Image Processing, ICIP 2014, no. October 2014, pp. 2418–2422 (2014)

    Google Scholar 

  23. Direct Multi-Scale Dual-Stream Network For Pedestrian Detection Sang-Il Jung and Ki-Sang Hong POSTECH Department of Electrical Engineering Pohang, Korea, pp. 156–160 (2017)

    Google Scholar 

  24. Zhang, C., Kim, J.: Multi-scale pedestrian detection using skip pooling and recurrent convolution. Multimed. Tools Appl. 78(2), 1719–1736 (2019)

    Article  Google Scholar 

  25. Ma, T., Ji, Q., Li, N.: Scene invariant crowd counting using multi-scales head detection in video surveillance. IET Image Process. 12(12), 2258–2263 (2018)

    Article  Google Scholar 

  26. Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: an evaluation of the state of the art. IEEE Trans. Pattern Anal. Mach. Intell. 34(4), 743–761 (2012)

    Article  Google Scholar 

  27. Li, C., Wang, X., Liu, W.: Neural features for pedestrian detection. Neurocomputing 238, 420–432 (2017)

    Article  Google Scholar 

  28. Gerónimo, D., López, A.M., Sappa, A.D., Graf, T.: Survey of pedestrian detection for advanced driver assistance systems. IEEE Trans. Pattern Anal. Mach. Intell. 32(7), 1239–1258 (2010)

    Article  Google Scholar 

  29. Ling, X., Yongjun, Z., Qian, W., Yuewei, L.: Improved local texture features for pedestrian detection. In: 2018 3rd IEEE International Conference on Image, Vision and Computing Comput, ICIVC 2018, pp. 60–65 (2018)

    Google Scholar 

  30. Rajnoha, M., Povoda, L., Masek, J., Burget, R., Dutta, M.K.: Pedestrian detection from low resolution public cameras in the wild. In: 2018 5th International Conference on Signal Processing and Integrated Networks, SPIN 2018, pp. 291–295 (2018)

    Google Scholar 

  31. Patil, S.S., Palanisamy, P.: Pedestrian classification in partial occlusion. In: 2017 4th International Conference on Signal Processing, Communication and Networking, ICSCN 2017, vol. 2, no. 2, pp. 2–7 (2017). https://doi.org/10.1109/icscn.2017.8085642

  32. Biswas, S.K., Milanfar, P.: Linear support tensor machine with LSK channels: pedestrian detection in thermal infrared images. IEEE Trans. Image Process. 26(9), 4229–4242 (2017)

    Article  MathSciNet  Google Scholar 

  33. Wang, X., Song, H., Cui, H.: Pedestrian abnormal event detection based on multi-feature fusion in traffic video. Optik (Stuttg) 154, 22–32 (2018)

    Article  Google Scholar 

  34. Dow, C., Lee, L., Huy, N.H., Wang, K.: A human recognition system for pedestrian crosswalk. In: HCI International 2018 - Posters' Extended Abstracts, vol. 852. Springer (2018)

    Google Scholar 

  35. Lee, Y.S., Chan, Y.M., Fu, L.C., Hsiao, P.Y.: Near-infrared-based nighttime pedestrian detection using grouped part models. IEEE Trans. Intell. Transp. Syst. 16(4), 1929–1940 (2015)

    Article  Google Scholar 

  36. Liu, L., Bao, H., Pan, W., Xu, C.: Night-time pedestrian detection based on temperature and HOGI feature in infra-red images. Int. J. Simul. Syst. Sci. Technol. 17(28), 14.1–14.7 (2016)

    Google Scholar 

  37. Al-Bsool, M.H.: Pedestrian recognition based on multi-scale weighted HOG. J. Comput. Sci. 14(11), 1431–1439 (2018)

    Article  Google Scholar 

  38. Ghaneizad, M., Kavehvash, Z., Aghajan, H.: Human detection in occluded scenes through optically inspired multi-camera image fusion. J. Opt. Soc. Am. A 34(6), 856 (2017)

    Article  Google Scholar 

  39. Sun, N., Jiang, F., Yan, H., Liu, J., Han, G.: Proposal generation method for object detection in infrared image. Infrared Phys. Technol. 81, 117–127 (2017)

    Article  Google Scholar 

  40. Lahmyed, R., El Ansari, M., Ellahyani, A.: A new thermal infrared and visible spectrum images-based pedestrian detection system. Multimed. Tools Appl. 78(12), 15861–15885 (2019)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, S.D.N.B. Vaishnav College for Women University of Madras, Chennai, India

    C. Victoria Priscilla & S. P. Agnes Sheila

Authors
  1. C. Victoria Priscilla
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. P. Agnes Sheila
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to C. Victoria Priscilla .

Editor information

Editors and Affiliations

  1. University of Technology Sydney, Sydney, Australia

    Lakhmi C. Jain

  2. CSIE Department, National Dong Hwa University, New Taipei City, Taiwan

    Sheng-Lung Peng

  3. Al-Balqa’ Applied University, Salt, Jordan

    Basim Alhadidi

  4. Department of Computer Science, Brainware University, Kolkata, West Bengal, India

    Souvik Pal

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Victoria Priscilla, C., Agnes Sheila, S.P. (2020). Pedestrian Detection - A Survey. In: Jain, L., Peng, SL., Alhadidi, B., Pal, S. (eds) Intelligent Computing Paradigm and Cutting-edge Technologies. ICICCT 2019. Learning and Analytics in Intelligent Systems, vol 9. Springer, Cham. https://doi.org/10.1007/978-3-030-38501-9_35

Download citation

Publish with us

Policies and ethics

Search

Navigation