Skip to main content

Advertisement

Springer Nature Link
Log in

Beyond estimating discrete directions of walk: a fuzzy approach

  • Special Issue Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Direction of walk of a pedestrian is vital information in various applications like visual surveillance, traffic monitoring and control, assisted living systems and automated car assistance system. Existing methods of direction of walk estimation exploit inter-frame and intra-frame features of a pedestrian frame sequence to classify the motion among predefined discrete direction classes. However, in order to achieve a robust method to estimate direction of walk, a strong analogy to justify an evident stationary or motion pattern as potential feature for direction estimation is essential. Discrete results of walk direction are famously used as it can be estimated in less time and are preferable for less precision sensitive scenario. However, the intra-frame feature that yields per-frame orientation is underutilized when the walk directions are subjected to discrete classes and hence there is a stringent need to go beyond discrete levels of direction and comment on specific walk direction angles at the same time maintaining a strong analogy to justify the potential of proposed features for direction of walk estimation. With this motivation, the article proposes a type-1 fuzzy approach over apposite inter-frame as well as intra-frame locomotion feature of pedestrian to yield precise direction of walk in terms of fuzzy directions beyond discrete levels of pedestrian walk directions. The method identifies eight directions as potential membership functions. Identified features are subjected to rule-based table for identification and removal of noisy orientation results, decision on membership function and their membership grade generation. The defuzzified result yields crisp direction of walk estimates beyond discrete levels of direction. The enhanced direction of walk estimation results is evident from qualitative comparison from existing research works and is also supported by the simulation performed over different datasets. The proposed method achieves 98.33%, 98.79%, and 100% Rank-2 balanced accuracy while applied on CASIA Dataset A, B, and NITR Conscious Walk Dataset respectively, which clearly points out its better performance than state of the art.

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
€32.70 /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 includes VAT (France)

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

Abbreviations

CASIA Dataset:

Institute of Automation Chinese Academy of Sciences Dataset

DR:

Dead Reckoning

GLMPC:

Global Local Motion Pattern Classification

GPS:

Global Positioning System

HMM:

Hidden Markov Model

HoG:

Histogram of Gradient

HoF:

Histogram of Flow

kNN:

k-Nearest Neighbors

LBP:

Local Binary Pattern

MEMS-IMU:

Micro-Electro-Mechanical System Inertial Measurement Unit

NITRCWD:

National Institute of Technology Rourkela Conscious Walk Dataset

SVM:

Support Vector Machine

LS-SVM:

Least Square Support Vector Machine

References

  1. Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A., Finocchio, M., Blake, A., Cook, M., Moore, R.: Real-time human pose recognition in parts from single depth images. Commun. ACM 56(1), 116–124 (2013). https://doi.org/10.1145/2398356.2398381

    Article  Google Scholar 

  2. Goel, D., Chen, T.: Pedestrian detection using global-local motion patterns. In: Asian Conference on Computer Vision (ACCV), pp. 220–229 (2007). https://doi.org/10.1007/978-3-540-76386-4_20

  3. Tao, J., Klette, R.: Integrated pedestrian direction classification using random decision forest. In: IEEE International Conference on Computer Vision (ICCV) Workshops, pp. 230–237 (2013). https://doi.org/10.1109/ICCVW.2013.38

  4. Raman, R., Sa, P.K., Majhi, B.: Occlusion prediction algorithms for multi-camera network. In: IEEE/ACM Sixth International Conference on Distributed Smart Cameras, pp. 1–6 (2012)

  5. Zhao, G., Takafumi, M., Shoji, K., Kenji, M.: Video based estimation of pedestrian walking direction for pedestrian protection system. J. Electron. 29(1–2), 72–81 (2012). https://doi.org/10.1007/s11767-012-0814-y

    Article  Google Scholar 

  6. Chen, C., Heili, A., Odobez, J.M.: Combined estimation of location and body pose in surveillance video. In: IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 5–10 (2011). https://doi.org/10.1109/AVSS.2011.6027284

  7. Raman, R., Sa, P.K., Majhi, B., Bakshi, S.: Direction estimation for pedestrian monitoring system in smart cities: an HMM based approach. IEEE Access 4, 5788–5808 (2016). https://doi.org/10.1109/ACCESS.2016.2608844

    Article  Google Scholar 

  8. Goto, K., Kidono, K., Kimura, Y., Naito, T.: Pedestrian detection and direction estimation by cascade detector with multi-classifiers utilizing feature interaction descriptor. In: IEEE Intelligent Vehicle Symposium (IV), pp. 224–229 (2011). https://doi.org/10.1109/IVS.2011.5940432

  9. Andriluka, M., Roth, S., Schiele, B.: Monocular 3D pose estimation and tracking by detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 623–630 (2010). https://doi.org/10.1109/CVPR.2010.5540156

  10. Pierard, S., Droogenbroeck, M.V.: Estimation of human orientation based on silhouettes and machine learning principles. In: International Conference on Pattern Recognition Applications and Methods (ICPRAM), pp. 51–60 (2012)

  11. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 1, pp. 886–893 (2005). https://doi.org/10.1109/CVPR.2005.177

  12. Mohan, A., Papageorgiou, C., Poggio, T.: Example-based object detection in images by components. IEEE Trans. Pattern Anal. Mach. Intell. 23(4), 349–361 (2001). https://doi.org/10.1109/34.917571

    Article  Google Scholar 

  13. Dalal, N., Triggs, B., Schmid, C.: Human detection using oriented histograms of flow and appearance. In: European Conference on Computer Vision (ECCV), pp. 428–441 (2006). https://doi.org/10.1007/11744047_33

  14. Gandhi, T., Trivedi, M.M.: Pedestrian collision avoidance systems: a survey of computer vision based recent studies. In: IEEE Intelligent Transportation Systems Conference, pp. 17–20 (2006). https://doi.org/10.1109/ROBOT.2001.932775

  15. Papageorgiou, C., Poggio, T.: A trainable system for object detection. Int. J. Comput. Vision 38(1), 15–33 (2000). https://doi.org/10.1023/A:1008162616689

    Article  MATH  Google Scholar 

  16. Shimizu, H., Poggio, T.: Direction estimation of pedestrian from multiple still images. In: IEEE Intelligent Vehicles Symposium, 2004, pp. 596–600 (2004). https://doi.org/10.1109/IVS.2004.1336451

  17. Nakajima, C., Pontil, M., Heisele, B., Poggio, T.: Full-body person recognition system. Pattern Recogn. 36, 1997–2006 (2003). https://doi.org/10.1016/S0031-3203(03)00061-X

    Article  MATH  Google Scholar 

  18. Gavrila, D.M., Munder, S.: Multi-cue pedestrian detection and tracking from a moving vehicle. Int. J. Comput. Vision 73(1), 41–59 (2007). https://doi.org/10.1007/s11263-006-9038-7

    Article  Google Scholar 

  19. Tuzel, Q., Porikli, F., Meer, P.: Human detection via classification on Riemannian manifolds. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2007). https://doi.org/10.1109/CVPR.2007.383197

  20. Raman, R., Sa, P.K., Baksi, S., Majhi, B.: Kinesiology-inspired estimation of pedestrian walk direction for smart surveillance. Future Gener. Comput. Syst. (2017). https://doi.org/10.1016/j.future.2017.10.033

  21. Gandhi, T., Trivedi, M.M.: Image based estimation of pedestrian orientation for improving path prediction. In: IEEE Intelligent Vehicles Symposium, pp. 506–511 (2008). https://doi.org/10.1109/IVS.2008.4621257

  22. Liu, H., Ma, L.: Online person orientation estimation based on classifier update. In: IEEE International Conference on Image Processing (ICIP), pp. 1568–1572 (2015). https://doi.org/10.1109/ICIP.2015.7351064

  23. Shimizu, H., Poggio, T.: Direction estimation of pedestrian from images. In: AI Memo 2003-020, Massachusetts Institute of Technology, pp. 1–11 (2003)

  24. Bensebaa, A., Larabi, S., Robertson, N.M.: Inferring heading direction from silhouettes. In: Developments in Medical Image Processing and Computational Vision, pp. 319–334 (2015). https://doi.org/10.1007/978-3-319-13407-9_19

  25. Lusi, I., Jacques Junior, J.C.S., Gorbova, J., BarÃş, X., Escalera, S., Demirel, H., Allik, J., Ozcinar, C., Anbarjafari, G.: Joint challenge on dominant and complementary emotion recognition using micro emotion features and head-pose estimation: Databases. In: IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pp. 809–813 (2017). https://doi.org/10.1109/FG.2017.102

  26. Lusi, I., Escarela, S., Anbarjafari, G.: SASE: RGB-depth database for human head pose estimation. In: European Conference on Computer Vision, vol. 9915, pp. 325–336 (2016). https://doi.org/10.1007/978-3-319-49409-8_26

  27. Toth, C., Grejner-Brzezinska, D.A., Moafipoor, S.: Pedestrian tracking and navigation using neural networks and fuzzy logic. In: IEEE International Symposium on Intelligent Signal Processing, pp. 1–6 (2007). https://doi.org/10.1109/WISP.2007.4447525

  28. Markis, D., Ellis, T.: Spatial and probabilistic modelling of pedestrian behaviour. Br. Mach. Vis. Conf. 2, 557–566 (2002)

    Google Scholar 

  29. Antonelli, G., Chiaverini, S., Fusco, G.: A fuzzy-logic-based approach for mobile robot path tracking. IEEE Trans. Fuzzy Syst. 15(2), 211–221 (2007). https://doi.org/10.1109/TFUZZ.2006.879998

    Article  Google Scholar 

  30. Castro, J.L., Delgado, M., Medina, J., Ruiz-Lozano, M.D.: An expert fuzzy system for predicting object collisions its application for avoiding pedestrian accidents. Expert Syst. Appl. 38(1), 486–494 (2011). https://doi.org/10.1016/j.eswa.2010.06.088

    Article  Google Scholar 

  31. Lavi, B., Ahmed, M.A.O.: Interactive fuzzy cellular automata for fast person re-identification. In: The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018), pp. 147–157 (2018). https://doi.org/10.1007/978-3-319-74690-6_15

  32. Liu, Z., Wang, X., Wang, J., Wang, F., Liu, Y., Wang, J.: Pedestrian movement intention identification model in mixed pedestrian-bicycle sections based on phase-field coupling theory. Adv. Mech. Eng. 10(2), 1–14 (2018). https://doi.org/10.1177/1687814017746515

    Article  Google Scholar 

  33. Nattharith, P., Güzel, M.S.: Machine vision and fuzzy logic-based navigation control of a goal-oriented mobile robot. Adapt. Behav. 24(3), 168–180 (2016). https://doi.org/10.1177/1059712316645845

    Article  Google Scholar 

  34. Vancheri, A., Giordano, P., Andrey, D.: Fuzzy logic based modeling of traffic flows induced by rerional shopping malls. Adv. Complex Syst. 17(0304), 1450017–1450050 (2014). https://doi.org/10.1142/S0219525914500179

    Article  MathSciNet  Google Scholar 

  35. Nguyen, L.V., La, H.M.: Real-time human foot motion localization algorithm with dynamic speed. IEEE Trans. Hum. Mach. Syst. 46(6), 822–833 (2016). https://doi.org/10.1109/THMS.2016.2586741

    Article  Google Scholar 

  36. Albusac, J., Vallejo, D., Castro-Schez, J.J., Gzlez-Morcillo, C.: An expert fuzzy system for improving safety on pedestrian crossings by means of visual feedback. Control Eng. Pract. 75, 38–54 (2018). https://doi.org/10.1016/j.conengprac.2018.03.008

    Article  Google Scholar 

  37. Pau, G., Campisi, T., Canale, A., Severino, A., Collotta, M., Tesoriere, G.: Smart pedestrian crossing management at traffic light junctions through a fuzzy-based approach. Future Intern (2018). https://doi.org/10.3390/fi10020015

    Article  Google Scholar 

  38. Dominguez, J.M.L., Sanguino, T.J.M.: Design, modelling, and implementation of a fuzzy controller for an intelligent road signaling system. Hindawi Complex. 1–14, 2018 (2018). https://doi.org/10.1155/2018/1849527

    Article  Google Scholar 

  39. Enzweiler, M., Eigenstetter, A., Schiele, B., Gavrila, D.M.: Multicue pedestrian classification with partial occlusion handling. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 990–997 (2010). https://doi.org/10.1109/CVPR.2010.5540111

  40. Lee, B.Y., Liew, L.H., Cheah, W.S., Wang, Y.C.: Occlusion handling in video object tracking: a survey. Proc. Int. Symp. Digit. Earth 18(1), 012–020 (2014). https://doi.org/10.1088/1755-1315/18/1/012097

    Article  Google Scholar 

  41. Raman, R., Sa, P.K., Majhi, B.: Direction prediction for avoiding occlusion in visual surveillance. Innov. Syst. Softw. Eng. 12(3), 201–214 (2016). https://doi.org/10.1007/s11334-016-0278-6

    Article  Google Scholar 

  42. Raman, R., Sa, P.K., Bakshi, S., Majhi, B.: Towards optimized placement of cameras for gait pattern recognition. In: International Conference on Communication, Computing and Security, Elsevier (ICCCS), pp. 1019–1025. (2012). https://doi.org/10.1016/j.protcy.2012.10.124

  43. Large, F., Vasquez, D., Fraichard, T., Laugier, C.: Avoiding cars and pedestrians using velocity obstacles and motion prediction. In: IEEE Intelligent Vehicle Symposium, pp. 375–379 (2004) https://doi.org/10.1109/IVS.2004.1336412

  44. Rehder, E., Kloeden, H., Stiller, C.: Head detection and orientation estimation for pedestrian safety. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pp. 2292–2297 (2014). https://doi.org/10.1109/ITSC.2014.6958057

  45. Ni, Q., Hernando, A.B.G., Cruz, I.P.: The elderly’s independent living in smart homes: a characterization of activities and sensing infrastructure survey to facilitate services development. Sensors 15, 11312–11362 (2015). https://doi.org/10.3390/s150511312

    Article  Google Scholar 

  46. CASIA Dataset. Available: http://www.csbr.ia.ac.cn/english/Gait%20 databases.asp

  47. NITR Conscious Walk Dataset. http://www.nitrkl.ac.in/Academic/ Academic_Centers/Data_Computer_Vision.aspx

  48. Baltieri, D., Vezzani, R., Cucchiara, R.: People orientation recognition by mixtures of wrapped distributions on random trees. In: European Conference on Computer Vision (ECCV), pp. 270–283 (2012). https://doi.org/10.1007/978-3-642-33715-4_20

  49. Liu, W., Zhang, Y., Tang, S., Tang, J., Hong, R., Li, J.: Accurate estimation of human body orientation from RGB-D sensors. IEEE Trans. Cybern. 43(5), 1442–1452 (2013). https://doi.org/10.1109/TCYB.2013.2272636

    Article  Google Scholar 

  50. Flohr, F., Dumitru-Guzu, M., Kooij, J.F.P., Gavrila, D.M.: Joint probabilistic pedestrian head and body orientation estimation. In: IEEE Intelligent Vehicles Symposium, pp. 617–622 (2014). https://doi.org/10.1109/IVS.2014.6856532

  51. Vieira, T., Faugeroux, R., Martinez, D., Lewiner, T.: Online human moves recognition through discriminative key poses and speed-aware action graphs. Mach. Vis. Appl. 28, 185–200 (2017). https://doi.org/10.1007/s00138-016-0818-y

    Article  Google Scholar 

  52. Hsu, S.-C., Huang, J.-Y., Kao, W.-C., Huang, C.-L.: Human body motion parameters capturing using Kinect. Mach. Vis. Appl. 26, 919–932 (2015). https://doi.org/10.1007/s00138-015-0710-1

    Article  Google Scholar 

  53. Vera, P., Monjaraz, S., Salas, J.: Counting pedestrians with a zenithal arrangement of depth cameras. Mach. Vis. Appl. 27, 303–315 (2016). https://doi.org/10.1007/s00138-015-0739-1

    Article  Google Scholar 

Download references

Acknowledgements

The research presented in this article is funded by Grant No. 12(5)/2012-ESD by Department of Electronics and Information Technology, Government of India. The work described in the article is extended from R. Raman, P. K. Sa, S. Bakshi, and B. Majhi, Kinesiology-inspired estimation of pedestrian walk direction for smart surveillance, Future Generation Computer Systems (2017) DOI: 10.1016/j.future.2017.10.033.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, 769 008, Odisha, India

    Rahul Raman, Pankaj Kumar Sa, Banshidhar Majhi & Sambit Bakshi

  2. Department of Computer Science and LIASD research Laboratory, University of Paris 8, Saint-Denis, France

    Larbi Boubchir

Authors
  1. Rahul Raman
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Larbi Boubchir
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pankaj Kumar Sa
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Banshidhar Majhi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sambit Bakshi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sambit Bakshi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Raman, R., Boubchir, L., Sa, P.K. et al. Beyond estimating discrete directions of walk: a fuzzy approach. Machine Vision and Applications 30, 901–917 (2019). https://doi.org/10.1007/s00138-018-0939-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-018-0939-6

Keywords

Search

Navigation