Kinects and human kinetics: A new approach for studying pedestrian behavior

Transportation Research Part C: Emerging Technologies

Volumn 48, Issue , 2014, Pages 212-228

  Seer, Stefan ^a,b   Brändle, Norbert ^a   Ratti, Carlo ^b  

^a AIT AUSTRIAN INSTITUTE OF TECHNOLOGY   (Austria)

^b MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Microsoft Kinect; Model calibration; Pedestrian simulation; People tracking; Pervasive computing; Ubiquitous sensing

Indexed keywords

OBJECT RECOGNITION; TRAJECTORIES; UBIQUITOUS COMPUTING;

MICROSOFT KINECT; MODEL CALIBRATION; PEDESTRIAN SIMULATION; PEOPLE TRACKING; UBIQUITOUS SENSING;

OBJECT TRACKING;

ALGORITHM; CALIBRATION; COMPUTER SIMULATION; PEDESTRIAN; TRAVEL BEHAVIOR; WALKING;

EID: 84907659163     PISSN: 0968090X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.trc.2014.08.012     Document Type: Article

References (62)

1. Antonini G., Bierlaire M., Weber M. Discrete choice models of pedestrian walking behavior. Transp. Res. Part B: Methodol. 2006, 40:667-687.
2. Baltieri, D., Vezzani, R., Cucchiara, R., 2011. 3DPes: 3D people dataset for surveillance and forensics. In: Proceedings 1st International ACM Workshop on Multimedia access to 3D Human Objects, Scottsdale, Arizona, USA. pp. 59-64.
3. Berclaz J., Fleuret F., Turetken E., Fua P. Multiple object tracking using K-shortest paths optimization. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 2011, 33:1806-1819.
4. Bernardin K., Stiefelhagen R. Evaluating multiple object tracking performance: the CLEAR MOT metrics. EURASIP J. Image Process. 2008, 2008:1:1-1:10.
5. Berrou J., Beecham J., Quaglia P., Kagarlis M., Gerodimos A. Calibration and validation of the legion simulation model using empirical data. Proceedings of the Conference on Pedestrian and Evacuation Dynamics (PED 2005) 2007, 167-181. Springer, Berlin, Heidelberg. N. Waldau, P. Gattermann, H. Knoflacher, M. Schreckenberg (Eds.).
6. Bob Fisher, 2010. Edinburgh Informatics Forum Pedestrian Database. (accessed October 2013). http://homepages.inf.ed.ac.uk/rbf/FORUMTRACKING/.
7. Boltes M., Seyfried A., Steffen B., Schadschneider A. Automatic extraction of pedestrian trajectories from video recordings. Proceedings of the Conference on Pedestrian and Evacuation Dynamics (PED 2008) 2010, 43-54. Springer, Berlin, Heidelberg. W.W.F. Klingsch, C. Rogsch, A. Schadschneider, M. Schreckenberg (Eds.).
8. Burstedde C., Klauck K., Schadschneider A., Zittartz J. Simulation of pedestrian dynamics using a 2-dimensional cellular automaton. Physica A 2001, 295:507-525.
9. Calderara S., Heinemann U., Prati A., Cucchiara R., Tishby N. Detecting anomalies in people's trajectories using spectral graph analysis. Comput. Vision Image Understanding 2011, 115:1099-1111.
10. Choi W., Pantofaru C., Savarese S. A general framework for tracking multiple people from a moving camera. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 2013, 35:1577-1591.
11. Daamen, W., Hoogendoorn, S.P., 2006. Free speed distributions for pedestrian traffic. In: Proceedings of the Transportation Research Board 85st Annual Meeting (TRB 2006), Washington D.C., USA. pp. 1-13.
12. Daamen, W., Hoogendoorn, S.P., 2012. Calibration of pedestrian simulation model for emergency doors for different pedestrian types. In: Proceedings of the Transportation Research Board 91st Annual Meeting (TRB 2012), Washington D.C., USA.
13. Davidich M., Köster G. Towards automatic and robust adjustment of human behavioral parameters in a pedestrian stream model to measured data. Safety Sci. 2012, 50:1253-1260.
14. de Boor C. A Practical Guide to Splines 2001, Springer-Verlag, New York. second ed.
15. Duda R., Hart P., Stork D. Pattern Classification 2001, Wiley.
16. Duives, D.C., Daamen, W., Hoogendoorn, S.P., 2013. State-of-the-art crowd motion simulation models. Transportation Research Part C: Emerging Technologies.
17. Eiter, T., Mannila, H., 1994. Computing Discrete Fréchet Distance. Technical Report CD-TR 94/64. Vienna University of Technology.
18. Eshel R., Moses Y. Tracking in a dense crowd using multiple cameras. Int. J. Comput. Vision 2010, 88:129-143.
19. Fleuret F., Berclaz J., Lengange R., Fua P. Multicamera people tracking with a probabilistic occupancy map. IEEE Trans. Pattern Anal. Mach. Intell. 2008, 30:267-282.
20. Forsyth D.A., Ponce J. Computer Vision: A Modern Approach 2002, Prentice Hall. 1 ed.
21. Frati, V., Prattichizzo, D., 2011. Using kinect for hand tracking and rendering in wearable haptics. In: Proceedings of the IEEE World Haptics Conference (WHC 2011), pp. 317-321.
22. Girshick R., Shotton J., Kohli P., Criminisi A., Fitzgibbon A. Efficient regression of general-activity human poses from depth images. Proceedings of the IEEE International Conference on Computer Vision (ICCV 2011) 2011, 415-422. IEEE Computer Society, Los Alamitos, CA, USA.
23. Guo R.Y., Wong S., Huang H.J., Zhang P., Lam W.H. A microscopic pedestrian-simulation model and its application to intersecting flows. Physica A: Stat. Mech. Appl. 2010, 389:515-526.
24. Heath K., Guibas L.J. Multi-Person tracking from sparse 3d trajectories in a camera sensor network. Proceedings of the Second ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC 2008) 2008, 1-9. IEEE.
25. Helbing D., Johansson A. Pedestrian, crowd and evacuation dynamics. Encycl. Complex. Syst. Sci. 2009, 16:6476-6495.
26. Helbing D., Molnár P. Social force model for pedestrian dynamics. Phys. Rev. E 1995, 51:4282-4286.
27. Helbing D., Molnár P. Self-Organization Phenomena in Pedestrian Crowds. Self-Organiz. Complex Struct. Individual Collective Dyn. 1997, 569-577.
28. Hirasaki E., Moore S., Raphan T., Cohen B. Effects of walking velocity on vertical head and body movements during locomotion. Exp. Brain Res. 1999, 127:117-130.
29. Hoogendoorn S., Daamen W. Microscopic parameter identification of pedestrian models and implications for pedestrian flow modelling. Transp. Res. Rec. 2006, 1982:57-64.
30. Hoogendoorn S.P., Bovy P.H.L. Pedestrian route-choice and activity scheduling theory and models. Transp. Res. Part B: Methodol. 2004, 38:169-190.
31. Hoogendoorn, S.P., Daamen, W., 2003. Extracting microscopic pedestrian characteristics from video data. In: Proceedings of the Transportation Research Board 82st Annual Meeting (TRB 2003), Washington D.C., USA. pp. 1-15.
32. Hoogendoorn S.P., Daamen W. Pedestrian behavior at bottlenecks. Transp. Sci. 2005, 39:147-159.
33. Izadi S., Kim D., Hilliges O., Molyneaux D., Newcombe R., Kohli P., Shotton J., Hodges S., Freeman D., Davison A., Fitzgibbon A. KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology (UIST 2011) 2011, 559-568. ACM, New York, NY, USA.
34. Javed O., Shafique K., Rasheed Z., Shah M. Modeling inter-camera spacetime and appearance relationships for tracking across non-overlapping views. Comput. Vision Image Understanding 2007, 109:146-162.
35. Johansson A., Helbing D. Analysis of empirical trajectory data of pedestrians. Proceedings of the Conference on Pedestrian and Evacuation Dynamics (PED 2008) 2010, 203-214. Springer, Berlin, Heidelberg. W.W.F. Klingsch, C. Rogsch, A. Schadschneider, M. Schreckenberg (Eds.).
36. Johansson A., Helbing D., Shukla P.K. Specification of the social force pedestrian model by evolutionary adjustment to video tracking data. Adv. Complex Syst. 2007, 10:271-288.
37. Klein W., Köster G., Meister A. Towards the calibration of pedestrian stream models. Proceedings of the 8th International Conference on Parallel Processing and Applied Mathematics: Part II 2010, 521-528. Springer-Verlag, Berlin, Heidelberg.
38. Konolige, K., Mihelich, P., 2012. Technical Description of Kinect Calibration. (accessed October 2013). http://www.ros.org/wiki/kinect_calibration/technical.
39. Lagarias J.C., Reeds J.A., Wright M.H., Wright P.E. Convergence properties of the Nelder-mead simplex method in low dimensions. SIAM J. Optimiz. 1998, 9:112-147.
40. Loy, C., Xiang, T., Gong, S., 2009. Multi-camera activity correlation analysis. In: Proceedings IEEE Intl. Conference on Computer Vision and Pattern Recognition (CVPR09), pp. 1988-1995.
41. Massey F.J. The Kolmogorov-Smirnov test for goodness of fit. J. Am. Stat. Assoc. 1951, 46:68-78.
42. Mavrinac A., Chen X., Tepe K. An automatic calibration method for stereo-based 3D distributed smart camera networks. Comput. Vision Image Understanding 2010, 114:952-962.
43. Microsoft Corp., 2012a. Kinect for Xbox 360. Redmond, WA, USA.
44. Microsoft Corp., 2012b. Microsoft Kinect for Windows SDK. (accessed October 2013). http://www.microsoft.com/en-us/kinectforwindows/.
45. Moussaïd M., Helbing D., Theraulaz G. How simple rules determine pedestrian behavior and crowd disasters. Proc. Natl. Acad. Sci. 2011, 108:6884-6888.
46. Moussaïd M., Perozo N., Garnier S., Helbing D., Theraulaz G. The walking behaviour of pedestrian social groups and its impact on crowd dynamics. PLoS One 2010, 5:e10047.
47. Noonan, P.J., Cootes, T.F., Hallett, W.A., Hinz, R., 2011. The design and initial calibration of an optical tracking system using the microsoft kinect. In: Proceedings of the IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC 2011), pp. 3614-3617.
48. Oh, S., Hoggs, A., Perero, A., Cuntoor, N., Chen, C., Lee, J., Mikherjee, S., Aggarwal, J., Lee, H., Davis, L., Swears, E., Wang, X., Ji, Q., Reddy, K., Shah, M., Vondrick, C., Pirsiavash, H., Ramanan, D., Yuen, J., Torralba, A., Song, B., Fong, A., Roy-Chowdhury, A., M., D., 2011. A Large-scale benchmark dataset for event recognition in surveillance video. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition (CVPR2011). IEEE.
49. Pellegrini S., Ess A., Schindler K., van Gool L. You'll never walk alone: modeling social behavior for multi-target tracking. Proceedings of the IEEE International Conference on Computer Vision (ICCV 2009) 2009, 261-268. IEEE Computer Society, Los Alamitos, CA, USA.
50. PETS, 2009. PETS Dataset - Performance Evaluation of Tracking and Surveillance. (accessed October 2013). http://www.cvg.rdg.ac.uk/PETS2009/.
51. Pflugfelder R., Bischof H. Localization and trajectory reconstruction in surveillance cameras with non overlapping views. IEEE Trans. Pattern Anal. Mach. Intell. 2010, 34:709-721.
52. Plaue M., Chen M., Bärwolff G., Schwandt H. Trajectory extraction and density analysis of intersecting pedestrian flows from video recordings. Proceedings of the 2011 ISPRS Conference on Photogrammetric Image Analysis (PIA 2011) 2011, 285-296. Springer, Berlin, Heidelberg.
53. PTV Group, 2013. PTV Viswalk. (accessed October 2013). http://vision-traffic.ptvgroup.com/en-us/products/ptv-viswalk/.
54. Rudloff C., Matyus T., Seer S., Bauer D. Can walking behavior be predicted? An analysis of the calibration and fit of pedestrian models. Transp. Res. Rec. 2011, 2264:101-109.
55. Savannah Simulations, 2013. SimWalk. (accessed October 2013). http://www.simwalk.com/.
56. Shotton J., Fitzgibbon A., Cook M., Sharp T., Finocchio M., Moore R., Kipman A., Blake A. Real-time human pose recognition in parts from a single depth image. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2011) 2011, 1297-1304. IEEE Computer Society, Los Alamitos, CA, USA.
57. Sochman J., Hogg D. Who knows who - inverting the social force model for finding groups. IEEE International Conference on Computer Vision Workshops (ICCV Workshops) 2011, IEEE.
58. Stauffer C., Grimson W.E.L. Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 2000, 22:747-757.
59. Sternig, S., Mauthner, T., Irschara, A., Roth, P., Bischof, H., 2011. Multi-camera Multi-object Tracking by Robust Hough-based Homography Projections, in: Proceedings of the Eleventh IEEE International Workshop on Visual Surveillance (ICCV Workshops), pp. 1689-1696.
60. United Nations, D.o.E., Affairs, S., 2012. World urbanization prospects: The 2011 revision highlights.
61. University of Cyprus Computer Graphics Lab, 2011. Crowds-by-Example data set. (accessed October 2013). http://https://graphics.cs.ucy.ac.cy/research/downloads/crowd-data.
62. Weiss, A., Hirshberg, D., Black, M., 2011. Home 3D body scans from noisy image and range data. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV 2011), Barcelona. pp. 1951-1958.