The current study examines the possibility of automatically detecting distracted pedestrians on crosswalks using their gait parameters. The methodology utilises recent findings in health science concerning the relationship between walking gait behaviour and cognitive abilities. Walking speed and gait variability are shown to be affected by the complexity of tasks (e.g. texting) that are performed during walking. Experiments are performed on a video data set from Surrey, British Columbia. The analysis relies on automated video-based data collection using computer vision. A sensitivity analysis is carried out to assess the quality of the selected features in improving the accuracy of the classification. Classification results show that the proposed approach is promising with around 80% correct detection rate. This research can benefit applications in several transportation related fields such as pedestrian facility planning, pedestrian simulation models as well as road safety programmes and legislative studies.

References

1. 1)
  - 20. Ismail, K., Sayed, T., Saunier, N.: ‘Automated analysis of pedestrian-vehicle conflicts: context for before-and-after studies’, Transp. Res. Rec. J. Transp. Res. Board, 2010, 2198, pp. 52–64 (doi: 10.3141/2198-07).
2. 2)
  - 35. Messelodi, S., Modena, C.M., Cattoni, G.: ‘Vision-based bicycle/motorcycle classification’, Pattern Recogn. Lett., 2007, 28, (13), pp. 1719–1726 (doi: 10.1016/j.patrec.2007.04.014).
3. 3)
  - 18. Woollacott, M., Shumway-Cook, A.: ‘Attention and the control of posture and gait: a review of an emerging area of research’, Gait Posture, 2002, 16, (1), pp. 1–14 (doi: 10.1016/S0966-6362(01)00156-4).
4. 4)
  - 23. Crowe, A., Samson, M.M., Hoitsma, M.J., van, A.A.: ‘The influence of walking speed on parameters of gait symmetry determined from ground reaction forces’, Hum. Mov. Sci., 1996, 15, (3), pp. 347–367 (doi: 10.1016/0167-9457(96)00005-X).
5. 5)
  - 5. Basch, C.H., Ethan, D., Rajan, S., Basch, C.E.: ‘Technology-related distracted walking behaviours in Manhattan's most dangerous intersections’, Injury Prevention, 2014, 20, (5), pp. 1–4 (doi: 10.1136/injuryprev-2013-041063).
6. 6)
  - 27. Zaki, M., Sayed, T.: ‘Using automated walking gait analysis for the identification of pedestrian attributes’, Transp. Res. C, Emerg. Technol., 2014, 48, pp. 16–36 (doi: 10.1016/j.trc.2014.08.004).
7. 7)
  - 6. Yogev-Seligmann Galit, J.M.H., Giladi, N.: ‘Do we always prioritize balance when walking? Towards an integrated model of task prioritization’, Mov. Disorders, 2012, 27, (6), pp. 765–770 (doi: 10.1002/mds.24963).
8. 8)
  - 1. Nasar, J.L., Troyer, D.: ‘Pedestrian injuries due to mobile phone use in public places’, Accident Anal. Prev., 2013, 57, pp. 91–95 (doi: 10.1016/j.aap.2013.03.021).
9. 9)
  - 2. Lamberg, E.M., Muratori, L.M.: ‘Cell phones change the way we walk’, Gait Posture, 2012, 35, (4), pp. 688–690 (doi: 10.1016/j.gaitpost.2011.12.005).
10. 10)
  - 21. Chaquet, J.M., Carmona, E.J., Fernandez-Caballero, A.: ‘A survey of video datasets for human action and activity recognition’, Comput. Vis. Image Underst., 2013, 117, (6), pp. 633–659 (doi: 10.1016/j.cviu.2013.01.013).
11. 11)
  - 28. Li, S., Sayed, T., Zaki, M., et al: ‘Automated collection of pedestrian data through computer vision techniques’, Transp. Res Rec. J, Transp. Res. Board, 2012, 2299, pp. 121–127 (doi: 10.3141/2299-13).
12. 12)
  - 36. Golyandina, A.: ‘Automatic extraction and forecast of time series cyclic components within the framework of SSA’. Proc. of the Fifth Workshop on Simulation, 2005, pp. 45–50.
13. 13)
  - 29. Hediyeh, H., Sayed, T., Zaki, M.H., Ismail, K.: ‘Automated analysis of pedestrian crossing speed behavior at scramble-phase signalized intersections using computer vision techniques’, Int. J. Sustain. Transp., 2014, 8, (5), pp. 382–397 (doi: 10.1080/15568318.2012.708098).
14. 14)
  - 19. White, J., Mwakalonge, J.L., Siuhi, S.: ‘Distracted walking: what we know and data needs.’ Transportation Research Board 93rd Annual Meeting, 2014, vol. 14-0038.
15. 15)
  - 10. Walker, E.J., Lanthier, S.N., Risko, E.F., Kingstone, A.: ‘The effects of personal music devices on pedestrian behaviourm’, Saf. Sci., 2012, 50, (1), pp. 123–128 (doi: 10.1016/j.ssci.2011.07.011).
16. 16)
  - 34. John, A., Ruina, A.: ‘Multiple walking speed-frequency relations are predicted by constrained optimization’, J. Theor. Biol., 2001, 209, (4), pp. 445–453 (doi: 10.1006/jtbi.2001.2279).
17. 17)
  - 26. Zaki, M., Sayed, T.: ‘A framework for automated road-users classification using movement trajectories’, Transp. Res. C, Emerg. Technol., 2013, 33, pp. 50–73 (doi: 10.1016/j.trc.2013.04.007).
18. 18)
  - 4. Bungum, T.J., Day, C., Henry, L.J.: ‘The association of distraction and caution displayed by pedestrians at a lighted crosswalk’, J. Community Health, 2005, 30, (4), pp. 269–279 (doi: 10.1007/s10900-005-3705-4).
19. 19)
  - 9. Schwebel, D.C., Stavrinos, D., Byington, K.W., Davis, T., O'Neal, E.E., Jong, D.D.: ‘Distraction and pedestrian safety: how talking on the phone, texting, and listening to music impact crossing the street’, Accident Anal. Prev., 2012, 45, pp. 266–271 (doi: 10.1016/j.aap.2011.07.011).
20. 20)
  - 39. Guyon, I., Elisseeff, A.: ‘An introduction to variable and feature selection’, J. Mach. Learn. Res., 2003, 3, pp. 1157–1182.
21. 21)
  - 16. Montero-Odasso, M., Casas, A., Hansen, K.T., et al: ‘Quantitative gait analysis under dual-task in older people with mild cognitive impairment: a reliability study’, J. Neuroeng. Rehabil., 2009, 6, (1), p. 35 (doi: 10.1186/1743-0003-6-35).
22. 22)
  - 17. Yogev-Seligmann, G., Rotem-Galili, Y., Mirelman, A., Dickstein, R., Giladi, N., Hausdorff, J.M.: ‘How does explicit prioritization alter walking during dual-task performance? Effects of age and sex on gait speed and variability’, Phys. Ther., 2010, 90, (2), pp. 177–186 (doi: 10.2522/ptj.20090043).
23. 23)
  - 37. 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. 225–227 (doi: 10.1016/S0966-6362(98)00009-5).
24. 24)
  - 11. Rubinstein, J.S., Meyer, D.E., Evans, J.E.: ‘Executive control of cognitive processes in task switching’, J. Exp. Psychol. Hum. Percept. Perform., 2001, 27, (4), p. 763 (doi: 10.1037/0096-1523.27.4.763).
25. 25)
  - 3. Smith, D.C., Schreiber, K.M., Saltos, A., Lichenstein, S.B., Lichenstein, R.: ‘Ambulatory cell phone injuries in the United States: an emerging national concern’, J. Saf. Res., 2013, 47, pp. 19–23 (doi: 10.1016/j.jsr.2013.08.003).
26. 26)
  - 24. Mori, H., Charkari, N.M., Matsushita, T.: ‘On-line vehicle and pedestrian detections based on sign pattern’, IEEE Trans. Ind. Electron., 1994, 41, (4), pp. 384–391 (doi: 10.1109/41.303788).
27. 27)
  - 33. Hediyeh, H., Sayed, T., Zaki, M.H.: ‘The use of gait parameters to evaluate pedestrian behavior at scramble-phase signalized intersections’, J. Adv. Transp., 49, (4), pp. 523–534 (doi: 10.1002/atr.1284).
28. 28)
  - 22. Popoola, O.P., Wang, K.: ‘Video-based abnormal human behavior recognition – a review’, IEEE Trans. Syst. Man Cybern. C Appl. Rev, 2012, 42, (6), pp. 865–878 (doi: 10.1109/TSMCC.2011.2178594).
29. 29)
  - 7. Byington, K.W., Schwebel, D.C.: ‘Effects of mobile Internet use on college student pedestrian injury risk’, Accident Anal. Prev., 2013, 51, pp. 78–83 (doi: 10.1016/j.aap.2012.11.001).
30. 30)
  - 32. Hediyeh, H., Sayed, T., Zaki, M.H., Mori, G.: ‘Pedestrian gait analysis using automated computer vision techniques’, Transportmetrica A Transp. Sci., 2014, 10, (3), pp. 214–232 (doi: 10.1080/18128602.2012.727498).
31. 31)
  - 8. Madden, M., Lenhart, A., Duggan, M., Cortesi, S., Gasser, U.: ‘Teens and technology 2013’ (Pew Internet & American Life Project, Washington, DC, 2013).
32. 32)
  - 31. 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. 57–67 (doi: 10.1139/cjce-2011-0456).
33. 33)
  - 13. Demura, S., Uchiyama, M.: ‘Influence of cell phone email use on characteristics of gait’, Eur. J. Sport Sci., 2009, 9, (5), pp. 303–309 (doi: 10.1080/17461390902853069).
34. 34)
  - 40. Schabrun, S.M., Hoorn, W.v.d., Moorcroft, A., Greenland, C., Hodges, P.W.: ‘Texting and walking: strategies for postural control and implications for safety’, PloS One, 2014, 9, (1), p. e84312 (doi: 10.1371/journal.pone.0084312).
35. 35)
  - 14. Bauby, C.E., Kuo, A.: ‘Active control of lateral balance in human walking’, J. Biomech., 2000, 33, (11), pp. 1433–1440 (doi: 10.1016/S0021-9290(00)00101-9).
36. 36)
  - 30. Saunier, N., Sayed, T.: ‘A feature-based tracking algorithm for vehicles in intersections’. IEEE Canadian Conf. on Computer and Robot Vision, 2006.
37. 37)
  - 15. Ebersbach, G., Dimitrijevic, M.R., Poewe, W.: ‘Influence of concurrent tasks on gait: a dual-task approach’, Perceptual Motor Skills, 1995, 81, (1), pp. 107–113 (doi: 10.2466/pms.1995.81.1.107).
38. 38)
  - 38. Mizuike, C., Ohgi, S., Morita, S.: ‘Analysis of stroke patient walking dynamics using a tri-axial accelerometer’, Gait Posture, 2009, 30, (1), pp. 60–64 (doi: 10.1016/j.gaitpost.2009.02.017).
39. 39)
  - 25. Saunier, N., Husseini, A.E., Ismail, K., Morency, C., Auberlet, J.-M., Sayed, T.: ‘Pedestrian stride frequency and length estimation in outdoor urban environments using video sensors.’, Transp. Res. Rec. J. Transp. Res. Board, 2011, 2264, pp. 138–147 (doi: 10.3141/2264-16).

Exploring walking gait features for the automated recognition of distracted pedestrians

References

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