http://iet.metastore.ingenta.com
1887

Automated class identification of modes of travel in shared spaces: a case study from India

Automated class identification of modes of travel in shared spaces: a case study from India

For access to this article, please select a purchase option:

Buy eFirst article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
— Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This paper presents a classification approach for road-user modes of travel. The classification does not assume well organized, and lane disciplined traffic. Instead, it relies on specific characteristics intrinsic for each road-user to predict the corresponding class. The classification relies on extracting the geometric and movement characteristics of road-users. As such, it is possible to classify road-users in shared space facilities and sites with high level of non-compliance. The classification is a multi-step procedure. First, movement features are used to discriminate between motorized and non-motorized road-users. Then, complementary features based on road-user geometry are added to differentiate between vehicles, rickshaws, powered two-wheelers, and buses. Experiments are performed on a video data set from a shared facility in New Delhi, India. A performance analysis demonstrated the robustness of the proposed classification method with a correct classification rate of up to 90 percent. By considering the movement attributes, the approach is tolerant to considerable variations in road-user physical details which often arises from choices of camera positions and partial occlusions. The research is part of the long-term goal to develop an automated video-based road safety and data collection system for developing countries.

References

    1. 1)
      • J. Pucher , Z.-r. Peng , N. Mittal .
        1. Pucher, J., Peng, Z.-r., Mittal, N., et al: ‘Urban transport trends and policies in China and India: impacts of rapid economic growth’, Transp. Rev., 2007, 27, (4), pp. 379410.
        . Transp. Rev. , 4 , 379 - 410
    2. 2)
      • V. Kanagaraj , G. Asaithambi , T. Toledo .
        2. Kanagaraj, V., Asaithambi, G., Toledo, T., et al: ‘Trajectory data and flow characteristics of mixed traffic’, Transp. Res. Rec., 2015, 2491, pp. 111.
        . Transp. Res. Rec. , 1 - 11
    3. 3)
      • Z. Li , H. Xiong , B. Coifman .
        3. Li, Z., Xiong, H., Coifman, B.: ‘Empirical innovation of computational dual-loop models for identifying vehicle classifications against varied traffic conditions’, Comput.-Aided Civ. Infrastruct. Eng., 2013, 28, (8), pp. 621634.
        . Comput.-Aided Civ. Infrastruct. Eng. , 8 , 621 - 634
    4. 4)
      • S. Rajab , M.O.A. Kalaa , H. Refai .
        4. Rajab, S., Kalaa, M.O.A., Refai, H.: ‘Classification and speed estimation of vehicles via tire detection using single-element piezoelectric sensor’, J. Adv. Transp., 2017, 50, (7), pp. 13661385..
        . J. Adv. Transp. , 7 , 1366 - 1385
    5. 5)
      • H. Weinblatt , E. Minge , S. Petersen .
        5. Weinblatt, H., Minge, E., Petersen, S.: ‘Length-based vehicle classification schemes and length bin boundaries’, Transp. Res. Rec., 2013, 2339, pp. 1929.
        . Transp. Res. Rec. , 19 - 29
    6. 6)
      • M. Brosnan , M. Petesch , J. Pieper .
        6. Brosnan, M., Petesch, M., Pieper, J., et al: ‘Validation of bicycle counts from pneumatic tube counters in mixed traffic flows’, Transp. Res. Rec., 2015, 2527, pp. 8089.
        . Transp. Res. Rec. , 80 - 89
    7. 7)
      • H. Cho , P. Rybski , W. Zhang .
        7. Cho, H., Rybski, P., Zhang, W.: ‘Vision-based bicyclist detection and tracking for intelligent vehicles’. IEEE Intelligent Vehicles Symp., San Diego, CA, USA, 2010, pp. 454461.
        . IEEE Intelligent Vehicles Symp. , 454 - 461
    8. 8)
      • S. Messelodi , C.M. Modena , G. Cattoni .
        8. Messelodi, S., Modena, C.M., Cattoni, G.: ‘Vision-based bicycle/motorcycle classification’, Pattern Recognit. Lett., 2007, 28, pp. 17191726.
        . Pattern Recognit. Lett. , 1719 - 1726
    9. 9)
      • S. Gupte , O. Masoud , R. Martin .
        9. Gupte, S., Masoud, O., Martin, R., et al: ‘Detection and classification of vehicles’, IEEE Trans. Intell. Transp. Syst., 2002, 3, (1), pp. 3747.
        . IEEE Trans. Intell. Transp. Syst. , 1 , 37 - 47
    10. 10)
      • B. Coifman , D. Beymer , P. McLauchlan .
        10. Coifman, B., Beymer, D., McLauchlan, P., et al: ‘A real-time computer vision system for vehicle tracking and traffic surveillance’, Transp. Res. C, Emerg. Technol., 1998, 6, (4), pp. 271288.
        . Transp. Res. C, Emerg. Technol. , 4 , 271 - 288
    11. 11)
      • J.-W. Hsieh , S.-H. Yu , Y.-S. Chen .
        11. Hsieh, J.-W., Yu, S.-H., Chen, Y.-S., et al: ‘Automatic traffic surveillance system for vehicle tracking and classification’, IEEE Trans. Intell. Transp. Syst., 2006, 7, (2), pp. 175187.
        . IEEE Trans. Intell. Transp. Syst. , 2 , 175 - 187
    12. 12)
      • Z. Sun , G. Bebis , R. Miller .
        12. Sun, Z., Bebis, G., Miller, R.: ‘On-road vehicle detection using optical sensors: a review’. IEEE Int. Conf. on Intelligent Transportation Systems, Washington, DC, USA, 2004, pp. 585590.
        . IEEE Int. Conf. on Intelligent Transportation Systems , 585 - 590
    13. 13)
      • S. Munder , D. Gavrila .
        13. Munder, S., Gavrila, D.: ‘An experimental study on pedestrian classification’, IEEE Trans. Pattern Anal. Mach. Intell., 2006, 28, (11), pp. 18631868.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 11 , 1863 - 1868
    14. 14)
      • S. Munder , C. Schnorr , D. Gavrila .
        14. Munder, S., Schnorr, C., Gavrila, D.: ‘Pedestrian detection and tracking using a mixture of view-based shape-texture models’, IEEE Trans. Intell. Transp. Syst., 2008, 9, (2), pp. 333343.
        . IEEE Trans. Intell. Transp. Syst. , 2 , 333 - 343
    15. 15)
      • N. Buch , J. Orwell , S. Velastin .
        15. Buch, N., Orwell, J., Velastin, S.: ‘Urban road user detection and classification using 3D wire frame models’, IET Comput. Vis., 2010, 4, (2), pp. 105116.
        . IET Comput. Vis. , 2 , 105 - 116
    16. 16)
      • B. Khanloo , F. Stefanus , M. Ranjbar .
        16. Khanloo, B., Stefanus, F., Ranjbar, M., et al: ‘A large margin framework for single camera offline tracking with hybrid cues’, Comput. Vis. Image Underst., 2012, 116, (6), pp. 676689.
        . Comput. Vis. Image Underst. , 6 , 676 - 689
    17. 17)
      • S. Yasutomi , H. Mori .
        17. Yasutomi, S., Mori, H.: ‘A method for discriminating of pedestrian based on rhythm’. Proc. of the IEEE/RSJ/GI Int. Conf. on Intelligent Robots and Systems, Munich, Germany, 1994, 2, pp. 988995.
        . Proc. of the IEEE/RSJ/GI Int. Conf. on Intelligent Robots and Systems , 988 - 995
    18. 18)
      • Y. Ran , R. Chellappa , Q. Zheng .
        18. Ran, Y., Chellappa, R., Zheng, Q.: ‘Finding gait in space and time’. IEEE Int. Conf. on Pattern Recognition, Hong Kong, China, 2006..
        . IEEE Int. Conf. on Pattern Recognition
    19. 19)
      • A. Teichman , J. Levinson , S. Thrun .
        19. Teichman, A., Levinson, J., Thrun, S.: ‘Towards 3D object recognition via classification of arbitrary object tracks’. IEEE Int. Conf. on Robotics and Automation (ICRA), Shanghai, China, 2011, pp. 40344041..
        . IEEE Int. Conf. on Robotics and Automation (ICRA) , 4034 - 4041
    20. 20)
      • T. Takahashi , H. Kim , S. Kamijo .
        20. Takahashi, T., Kim, H., Kamijo, S.: ‘Urban road user classification framework using local feature descriptors and HMM’. 2012 15th Int. IEEE Conf. on Intelligent Transportation Systems (ITSC), Anchorage, AK, USA, 2012, pp. 6772..
        . 2012 15th Int. IEEE Conf. on Intelligent Transportation Systems (ITSC) , 67 - 72
    21. 21)
      • C. Curio , J. Edelbrunner , T. Kalinke .
        21. Curio, C., Edelbrunner, J., Kalinke, T., et al: ‘Walking pedestrian recognition’. IEEE/IEEJ/JSAI Int. Conf. on Intelligent Transportation Systems, 1999, 1, (3), pp. 292297.
        . IEEE/IEEJ/JSAI Int. Conf. on Intelligent Transportation Systems , 3 , 292 - 297
    22. 22)
      • L. Boudet , S. Mideneta .
        22. Boudet, L., Mideneta, S.: ‘Pedestrian crossing detection based on evidential fusion of video-sensors’, Transp. Res. C, Emerg. Technol., 2009, 17, (5), pp. 484497.
        . Transp. Res. C, Emerg. Technol. , 5 , 484 - 497
    23. 23)
      • M. Enzweiler , D. Gavrila .
        23. Enzweiler, M., Gavrila, D.: ‘Monocular pedestrian detection: survey and experiments’, IEEE Trans. Pattern Anal. Mach. Intell., 2009, 31, (12), pp. 21792195.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 12 , 2179 - 2195
    24. 24)
      • T. Gandhi , M. Trivedi .
        24. Gandhi, T., Trivedi, M.: ‘Pedestrian protection systems: issues, survey, and challenges’, IEEE Trans. Intell. Transp. Syst., 2007, 8, (3), pp. 413430.
        . IEEE Trans. Intell. Transp. Syst. , 3 , 413 - 430
    25. 25)
      • S. Messelodi , C.M. Modena , M. Zanin .
        25. Messelodi, S., Modena, C.M., Zanin, M.: ‘A computer vision system for the detection and classification of vehicles at urban road intersections’, J. Pattern Anal. Appl., 2005, 8, pp. 1731.
        . J. Pattern Anal. Appl. , 17 - 31
    26. 26)
      • Y.-B. Lin , C.-P. Young .
        26. Lin, Y.-B., Young, C.-P.: ‘High-precision bicycle detection on single side-view image based on the geometric relationship’, Pattern Recognit., 2017, 63, pp. 334354.
        . Pattern Recognit. , 334 - 354
    27. 27)
      • G. Somasundaram , V. Morellas , N. Papanikolopoulos .
        27. Somasundaram, G., Morellas, V., Papanikolopoulos, N.: ‘Counting pedestrians and bicycles in traffic scenes’. Int. IEEE Conf. on Intelligent Transportation Systems, St. Louis, MO, USA, October 2009, pp. 16..
        . Int. IEEE Conf. on Intelligent Transportation Systems , 1 - 6.
    28. 28)
      • Y. Zhang , Q. Ling .
        28. Zhang, Y., Ling, Q.: ‘Bicycle detection based on multi-feature and multi-frame fusion in low-resolution traffic videos’, arXiv preprint2017, 1706.03309..
        .
    29. 29)
      • H. Hu , P. Tao , Z. Gao .
        29. Hu, H., Tao, P., Gao, Z., et al: ‘Vision-based bicycle detection using multiscale block local binary pattern’, Math. Probl. Eng., 2014, 2014, pp. 17.
        . Math. Probl. Eng. , 1 - 7
    30. 30)
      • X.L.L.F.F.J.W. Li , H. Xiong , M. Bernhard .
        30. Li, X.L.L.F.F.J.W., Xiong, H., Bernhard, M., et al: ‘A unified framework for concurrent pedestrian and cyclist detection’, IEEE Trans. Intell. Transp. Syst., 2017, 18, (2), pp. 269281.
        . IEEE Trans. Intell. Transp. Syst. , 2 , 269 - 281
    31. 31)
      • J. Yan , Q. Ling , Y. Zhang .
        31. Yan, J., Ling, Q., Zhang, Y., et al: ‘An adaptive bicycle detection algorithm based on multi-Gaussian models’, J. Comput. Inf. Syst., 2013, 9, (24), pp. 1007510083.
        . J. Comput. Inf. Syst. , 24 , 10075 - 10083
    32. 32)
      • B. Ling , D.R. Gibson , D. Middleton .
        32. Ling, B., Gibson, D.R., Middleton, D.: ‘Motorcycle detection and counting using stereo camera, IR camera, and microphone array’. Int. Society for Optics and Photonics, San Diego, CA, USA, 2013.
        . Int. Society for Optics and Photonics
    33. 33)
      • M. Bertozzi , A. Broggi , M. Cellario .
        33. Bertozzi, M., Broggi, A., Cellario, M., et al: ‘Artificial vision in road vehicles’, Proc. IEEE, 2002, 90, (7), pp. 12581271.
        . Proc. IEEE , 7 , 1258 - 1271
    34. 34)
      • J. Candamo , M. Shreve , D. Goldgof .
        34. Candamo, J., Shreve, M., Goldgof, D., et al: ‘Understanding transit scenes: a survey on human behavior-recognition algorithms’, IEEE Trans. Intell. Transp. Syst., 2010, 11, (1), pp. 206224.
        . IEEE Trans. Intell. Transp. Syst. , 1 , 206 - 224
    35. 35)
      • S. Dodge , R. Weibel , E. Forootan .
        35. Dodge, S., Weibel, R., Forootan, E.: ‘Revealing the physics of movement: comparing the similarity of movement characteristics of different types of moving objects’, Comput. Environ. Urban Syst., 2009, 33, (6), pp. 419434.
        . Comput. Environ. Urban Syst. , 6 , 419 - 434
    36. 36)
      • J. Moore , J. Kooijman , A. Schwab .
        36. Moore, J., Kooijman, J., Schwab, A.: ‘Rider motion identification during normal bicycling by means of principal component analysis’, Multibody Syst. Dyn., 2011, 25, pp. 225244.
        . Multibody Syst. Dyn. , 225 - 244
    37. 37)
      • J. Frank , S. Mannor , D. Precup .
        37. Frank, J., Mannor, S., Precup, D.: ‘Activity and gait recognition with time-delay embeddings’. Proc. of the AAAI Conf. on Artificial Intelligence, Atlanta, GA, USA, 2010..
        . Proc. of the AAAI Conf. on Artificial Intelligence
    38. 38)
      • S. Atev , G. Miller , N. Papanikolopoulos .
        38. Atev, S., Miller, G., Papanikolopoulos, N.: ‘Clustering of vehicle trajectories’, IEEE Trans. Intell. Transp. Syst., 2010, 11, (3), pp. 647657.
        . IEEE Trans. Intell. Transp. Syst. , 3 , 647 - 657
    39. 39)
      • X. Wang , M. Xiaoxu , W. Grimson .
        39. Wang, X., Xiaoxu, M., Grimson, W.: ‘Unsupervised activity perception in crowded and complicated scenes using hierarchical Bayesian models’, IEEE Trans. Pattern Anal. Mach. Intell., 2009, 31, (3), pp. 539555.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 3 , 539 - 555
    40. 40)
      • B. Morris , M. Trivedi .
        40. Morris, B., Trivedi, M.: ‘A survey of vision-based trajectory learning and analysis for surveillance’, IEEE Trans. Circuits Syst. Video Technol., 2008, 18, (8), pp. 11141127.
        . IEEE Trans. Circuits Syst. Video Technol. , 8 , 1114 - 1127
    41. 41)
      • B. Morris , M. Trivedi .
        41. Morris, B., Trivedi, M.: ‘Learning trajectory patterns by clustering: experimental studies and comparative evaluation’. IEEE Computer Society Computer Vision and Pattern Recognition, Miami, FL, USA, 2009, pp. 312319.
        . IEEE Computer Society Computer Vision and Pattern Recognition , 312 - 319
    42. 42)
      • Z. Zhang , K. Huang , T. Tan .
        42. Zhang, Z., Huang, K., Tan, T.: ‘Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes’. Int. Conf. on Pattern Recognition, 2006, 3, pp. 11351138.
        . Int. Conf. on Pattern Recognition , 1135 - 1138
    43. 43)
      • N. Saunier , A.E. Husseini , K. Ismail .
        43. Saunier, N., Husseini, A.E., Ismail, K., et al: ‘Pedestrian stride frequency and length estimation in outdoor urban environments using video sensors.’, Transp. Res. Rec., 2011, 2264, pp. 138147.
        . Transp. Res. Rec. , 138 - 147
    44. 44)
      • M. Zaki , T. Sayed .
        44. Zaki, M., Sayed, T.: ‘A framework for automated road-users classification using movement trajectories’, Transp. Res. C, Emerg. Technol., 2013, 33, pp. 5073.
        . Transp. Res. C, Emerg. Technol. , 50 - 73
    45. 45)
      • M.H. Zaki , T. Sayed .
        45. Zaki, M.H., Sayed, T.: ‘Automatic classification of road-user travel modes in a mixed traffic roundabout’. TRB 92nd Annual Meeting Compendium of Papers, Washington, DC, USA, 2013, pp. 120..
        . TRB 92nd Annual Meeting Compendium of Papers , 1 - 20
    46. 46)
      • K. Ismail , T. Sayed , N. Saunier .
        46. Ismail, K., Sayed, T., Saunier, N.: ‘Automated analysis of pedestrian-vehicle conflicts: a context for before-and-after studies’, Transp. Res. Rec., 2010, 2198, pp. 5264.
        . Transp. Res. Rec. , 52 - 64
    47. 47)
      • K. Ismail , T. Sayed , N. Saunier .
        47. Ismail, K., Sayed, T., Saunier, N.: ‘A methodology for precise camera calibration for data collection applications in urban traffic scenes’, Can. J. Civ. Eng., 2013, 40, (1), pp. 5767.
        . Can. J. Civ. Eng. , 1 , 57 - 67
    48. 48)
      • D. Hamad , P. Biela .
        48. Hamad, D., Biela, P.: ‘Introduction to spectral clustering’. Int. Conf. on Information and Communication Technologies: From Theory to Applications, Damascus, Syria, 2008, pp. 16.
        . Int. Conf. on Information and Communication Technologies: From Theory to Applications , 1 - 6
    49. 49)
      • D. Verma , M. Meil . (2003)
        49. Verma, D., Meil, M.: ‘A comparison of spectral clustering algorithms’. Technical Report, University of Washington, Seattle, 2003.
        .
    50. 50)
      • Z. Lu , M. Carreira-Perpinan .
        50. Lu, Z., Carreira-Perpinan, M.: ‘Constrained spectral clustering with affinity propagation’. IEEE Conf. on Computer Vision and Pattern Recognition, Anchorage, AK, USA, 2008, pp. 18.
        . IEEE Conf. on Computer Vision and Pattern Recognition , 1 - 8
    51. 51)
      • M. Zaki , T. Sayed , M. Esawey .
        51. Zaki, M., Sayed, T., Esawey, M.: ‘A mixed urban traffic road-users classification based on automated video data analysis’, Adv. Transp. Stud., 2015, 35, pp. 5570.
        . Adv. Transp. Stud. , 55 - 70
    52. 52)
      • M.H. Zaki , T. Sayed , G. Mori .
        52. Zaki, M.H., Sayed, T., Mori, G.: ‘Classifying road users in urban scenes using movement patterns’, ASCE J. Comput. Civil Eng., 2012, 27, (4), pp. 395406..
        . ASCE J. Comput. Civil Eng. , 4 , 395 - 406
    53. 53)
      • N. Saunier , T. Sayed .
        53. Saunier, N., Sayed, T.: ‘A feature-based tracking algorithm for vehicles in intersections’. The 3rd Canadian Conf. on Computer and Robot Vision, Quebec City, Canada, 2006.
        . The 3rd Canadian Conf. on Computer and Robot Vision
    54. 54)
      • A. John , A. Ruina .
        54. John, A., Ruina, A.: ‘Multiple walking speed–frequency relations are predicted by constrained optimization’, J. Theor. Biol., 2001, 209, (4), pp. 445453.
        . J. Theor. Biol. , 4 , 445 - 453
    55. 55)
      • N. Sekiya , H. Nagasaki .
        55. Sekiya, N., Nagasaki, H.: ‘Reproducibility of the walking patterns of normal young adults: test-retest reliability of the walk ratio (step-length/step-rate).’, Gait Posture, 1998, 7, (3), pp. 225227.
        . Gait Posture , 3 , 225 - 227
    56. 56)
      • A. Tageldin , T. Sayed , X. Wang .
        56. Tageldin, A., Sayed, T., Wang, X.: ‘Can time proximity measures be used as safety indicators in all driving cultures? A case study of motorcycle safety in China’, Transp. Res. Rec., 2015, 2520, pp. 165174.
        . Transp. Res. Rec. , 165 - 174
    57. 57)
      • C. Mizuike , S. Ohgi , S. Morita .
        57. Mizuike, C., Ohgi, S., Morita, S.: ‘Analysis of stroke patient walking dynamics using a tri-axial accelerometer’, Gait Posture, 2009, 30, (1), pp. 6064.
        . Gait Posture , 1 , 60 - 64
    58. 58)
      • H.B. Menz , S.R. Lord , R.C. Fitzpatrick .
        58. Menz, H.B., Lord, S.R., Fitzpatrick, R.C.: ‘Acceleration patterns of the head and pelvis when walking on level and irregular surfaces’, Gait Posture, 2003, 18, pp. 3546.
        . Gait Posture , 35 - 46
    59. 59)
      • I. Guyon , A. Elisseeff .
        59. Guyon, I., Elisseeff, A.: ‘An introduction to variable and feature selection’, J. Mach. Learn. Res., 2003, 3, pp. 11571182.
        . J. Mach. Learn. Res. , 1157 - 1182
    60. 60)
      • H. Peng , F. Long , C. Ding .
        60. Peng, H., Long, F., Ding, C.: ‘Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy’, IEEE Trans. Pattern Anal. Mach. Intell., 2005, 27, (8), pp. 12261238.
        . IEEE Trans. Pattern Anal. Mach. Intell. , 8 , 1226 - 1238
    61. 61)
      • N.X. Vinh , J. Epps , J. Bailey .
        61. Vinh, N.X., Epps, J., Bailey, J.: ‘Information theoretic measures for clusterings comparison: variants, properties, normalization and correction for chance’, J. Mach. Learn. Res., 2010, 11, pp. 28372854.
        . J. Mach. Learn. Res. , 2837 - 2854
    62. 62)
      • L.Z. manor , P. Perona .
        62. manor, L.Z., Perona, P.: ‘Self-tuning spectral clustering’, Adv. Neural Inf. Process. Syst., 2004, 17, pp. 16011608.
        . Adv. Neural Inf. Process. Syst. , 1601 - 1608
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2017.0099
Loading

Related content

content/journals/10.1049/iet-its.2017.0099
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address