Skip to main content
SpringerLink
Log in

Shadow removal for pedestrian detection and tracking in indoor environments

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

This paper presents a method of shadow removal to improve the accuracy of pedestrian detection and tracking in indoor environments. The proposed method can be divided into four steps: building a background model which can be automatically updated, extract moving objects region, eliminating moving objects shadows, classifying and track pedestrians. The background model is built with pixel value and the updating of Gussian. The approach for real time background-foreground extraction is used to extract pedestrian region that may contains multiple shadows. In the gray histogram space, based on the depth value of the gray images, a reasonable threshold is set to remove shadows from various pedestrians. In this work, we propose a methodology using the foreground frames without shadows to detect and track the pedestrians across training datasets. Comparative experimental results show that our method is capable of dealing with shadows and detecting moving pedestrians in cluttered environments.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Chen B, Lei Y (2004) Indoor and outdoor people detection and shadow suppression by exploiting HSV color information. In: CIT’04. The Fourth International Conference on Computer and Information Technology. IEEE

  2. Chen C, Aggarwal JK (2010) Human shadow removal with unknown light source. In: 20th International Conference on Pattern Recognition (ICPR). IEEE

  3. Choi J, Yoo YJ, Choi JY (2010) Adaptive shadow estimator for removing shadow of moving object. Comput Vis Image Underst 114(9):1017–1029

    Article  Google Scholar 

  4. Cheung SS, Kamath C (2004) Robust techniques for background subtraction in urban traffic video. In: Proceedings of SPIE

  5. Cheng HD, Chen YH, Jiang XH (2000) Thresholding using two-dimensional histogram and fuzzy entropy principle. IEEE Trans Image Process 9(4):732–735

    Article  Google Scholar 

  6. Dollar P et al (2012) Pedestrian detection: An evaluation of the state of the art. IEEE Trans Pattern Anal Mach Intell 34(4):743–761

    Article  Google Scholar 

  7. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. IEEE

  8. Everingham M et al (2010) The pascal visual object classes (voc) challenge. Int J Comput Vis 88(2):303–338

    Article  Google Scholar 

  9. Gavrila DM, Giebel J, Munder S (2004) Vision-based pedestrian detection: The protector system. In: Intelligent Vehicles Symposium. IEEE

  10. Guo L et al (2010) Study on pedestrian detection and tracking with monocular vision. In: 2nd International Conference on Computer Technology and Development (ICCTD). IEEE

  11. Huerta I et al (2007) Detection and removal of chromatic moving shadows in surveillance scenarios. In: Computer Vision, IEEE 12th International Conference on 2009. IEEE

  12. Horn BK, Schunck BG (1981) Determining optical flow. Artif Intell 17 (1):185–203

    Article  Google Scholar 

  13. Hsieh J et al (2003) Shadow elimination for effective moving object detection by Gaussian shadow modeling. Image Vis Comput 21(6):505–516

    Article  Google Scholar 

  14. Hurney P et al (2015) Night-time pedestrian classification with histograms of oriented gradients-local binary patterns vectors. Intelligent Transport Systems, IET 9.1:75–85

  15. Jeong S, Kang S, Kim J (2013) Vehicle detection based on the use of shadow region and edge. In: Fifth International Conference on Digital Image Processing. International Society for Optics and Photonics

  16. Jia Y et al (2013) A novel moving cast shadow detection of vehicles in traffic scene. In: Intelligent Science and Intelligent Data Engineering. Springer, pp 115–124

  17. Ji-Hong P, Wei-Xin X (1999) Adaptive multi thresholds image segmentation based on potential function clustering. Chin J Comput 22(7):758–762

    Google Scholar 

  18. Kurup U et al (2012) Predicting and Classifying Pedestrian Behavior Using an Integrated Cognitive Architecture. Proceedings Behavior Representation in Modeling Simulation (BRIMS)

  19. Kelly P (2008) Pedestrian detection and tracking using stereo vision techniques. Dublin City University

  20. Leone A, Distante C, Buccolieri F (2006) A shadow elimination approach in video-surveillance context. Pattern Recogn Lett 27(5):345–355

    Article  Google Scholar 

  21. Liu Y, Bin Z (2010) The improved moving object detection and shadow removing algorithms for video surveillance. In: International Conference on Computational Intelligence and Software Engineering (CiSE). IEEE

  22. Leone A, Distante C (2007) Shadow detection for moving objects based on texture analysis. Pattern Recogn 40(4):1222–33

    Article  MATH  Google Scholar 

  23. Munder S, Schnorr C, Gavrila DM (2008) Pedestrian detection and tracking using a mixture of view-based shapeCtexture models. IEEE Trans Intell Transp Syst 9 (2):333–343

    Article  Google Scholar 

  24. Mohan A, Papageorgiou C, Poggio T (2001) Example-based object detection in images by components. IEEE Trans Pattern Anal Mach Intell 23(4):349–361

    Article  Google Scholar 

  25. Maddalena L, Petrosino A (2008) A self-organizing approach to background subtraction for visual surveillance applications. IEEE Trans Image Process 17 (7):1168–1177

    Article  MathSciNet  Google Scholar 

  26. Otsu N (1975) A threshold selection method from gray-level histograms. Automatica 11(285-296):23–27

    Google Scholar 

  27. Singh VK, Wu B, Nevatia R (2008) Pedestrian tracking by associating tracklets using detection residuals, Motion and video Computing. WMVC 2008 IEEE Workshop on IEEE:1–8

  28. Saadat S, Teknomo K (2011) Automation of pedestrian tracking in a crowded situation. In: Pedestrian and Evacuation Dynamics. Springer, pp 231–239

  29. Shrivastava A, Patel VM, Pillai JK, Chellappa R (2015) Generalized dictionaries for multiple instance learning. Int J Comput Vis 114(2-3):288–305

    Article  MathSciNet  Google Scholar 

  30. Wang L et al (2007) Object detection combining recognition and segmentation. Comput Vis CACCV:189–199

    Google Scholar 

  31. Wang S, Su T, Lai S (2011) Detecting moving objects from dynamic background with shadow removal. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE

  32. Wang J et al (2004) Shadow detection and removal for traffic images. In: IEEE International Conference on Networking, Sensing and Control. IEEE

  33. Wu M, Lin C, Chang C (2007) Brain tumor detection using color-based k-means clustering segmentation. In: IIHMSP 2007. Third International Conference on Intelligent Information Hiding and Multimedia Signal Processing. IEEE

  34. Xu L, Landabaso JL, Pards M (2005) Shadow removal with blob-based morphological reconstruction for error correction. In: IEEE International Conference on Acoustics, Speech and Signal Processing

  35. Xu T et al (2012) A fast and robust pedestrian detection framework based on static and dynamic information. In: IEEE International Conference on Multimedia and Expo (ICME). IEEE

  36. Ye J, Gao T, Zhang J (2012) Moving object detection with background subtraction and shadow removal. In: 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE

  37. Ye Q, Han Z, Jiao J et al (2013) Human detection in images via piecewise linear support vector machines[J]. IEEE Trans Image Process 22(2):778–789

    Article  MathSciNet  Google Scholar 

  38. Zhou Y, Sun L, Zhang J A shadow elimination method based on color and texture. In: 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS). IEEE

Download references

Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Xiamen University, Xiamen, China

    Lingxiang Zheng, Xiaoyang Ruan, Yunbiao Chen & Minzheng Huang

Authors
  1. Lingxiang Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaoyang Ruan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yunbiao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Minzheng Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lingxiang Zheng.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zheng, L., Ruan, X., Chen, Y. et al. Shadow removal for pedestrian detection and tracking in indoor environments. Multimed Tools Appl 76, 18321–18337 (2017). https://doi.org/10.1007/s11042-016-3880-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-3880-6

Keywords

Search

Navigation