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

access icon openaccess Multiple human tracking in RGB-depth data: a survey

Loading full text...

Full text loading...

/deliver/fulltext/iet-cvi/11/4/IET-CVI.2016.0178.html;jsessionid=8s0sejp8uu2c.x-iet-live-01?itemId=%2fcontent%2fjournals%2f10.1049%2fiet-cvi.2016.0178&mimeType=html&fmt=ahah

References

    1. 1)
      • 1. Wang, X.: ‘Intelligent multi-camera video surveillance: a review’, Pattern Recognit. Lett., 2013, 34, (1), pp. 319.
    2. 2)
      • 2. Zabulis, X., Grammenos, D., Sarmis, T., et al: ‘Multicamera human detection and tracking supporting natural interaction with large-scale displays’, Mach. Vis. Appl., 2013, 24, (2), pp. 319336.
    3. 3)
      • 3. Cardinaux, F., Bhowmik, D., Abhayaratne, C., et al: ‘Video based technology for ambient assisted living: a review of the literature’, J. Ambient Intell. Smart Environ., 2011, 3, (3), pp. 253269.
    4. 4)
      • 4. Chaaraoui, A.A., Climent-Prez, P., Flrez-Revuelta, F.: ‘A review on vision techniques applied to human behaviour analysis for ambient-assisted living’, Expert Syst. Appl., 2012, 39, (12), pp. 1087310888.
    5. 5)
      • 5. Geronimo, D., Lopez, A., Sappa, A., et al: ‘Survey of pedestrian detection for advanced driver assistance systems’, IEEE Trans. Pattern Anal. Mach. Intell., 2010, 32, (7), pp. 12391258.
    6. 6)
      • 6. Lu, W.-L., Ting, J.-A., Little, J., et al: ‘Learning to track and identify players from broadcast sports videos’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (7), pp. 17041716.
    7. 7)
      • 7. Microsoft Corporporation. Kinect for Xbox 360, 2009.
    8. 8)
      • 8. Asustek Computer Inc. Xtion PRO LIVE, 2009.
    9. 9)
      • 9. Han, J., Shao, L., Xu, D., et al: ‘Enhanced computer vision with Microsoft Kinect sensor: a review’, IEEE Trans. Cybern., 2013, 43, (5), pp. 13181334.
    10. 10)
      • 10. Dollar, P., Wojek, C., Schiele, B., et al: ‘Pedestrian detection: an evaluation of the state of the art’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (4), pp. 743761.
    11. 11)
      • 11. Luo, W., Zhao, X., Kim, T.: ‘Multiple object tracking: a review’, CoRR abs/1409.7618, 2014, Pre-Print Version. URL http://arxiv.org/abs/1409.7618.
    12. 12)
      • 12. Chen, L., Wei, H., Ferryman, J.: ‘A survey of human motion analysis using depth imagery’, Pattern Recognit. Lett., 2013, 34, (15), pp. 19952006.
    13. 13)
      • 13. Zhang, J., Li, W., Ogunbona, P.O., et al: ‘RGB-D-based action recognition datasets: a survey’, Pattern Recogn., 2016, 60, pp. 86105.
    14. 14)
      • 14. Suarez, J., Murphy, R.: ‘Hand gesture recognition with depth images: a review’. RO-MAN, 2012, 2012, pp. 411417.
    15. 15)
      • 15. Endres, F., Hess, J., Sturm, J., et al: ‘3-D mapping with an RGB-D camera’, IEEE Trans. Robot., 2014, 30, (1), pp. 177187.
    16. 16)
      • 16. Enzweiler, M., Gavrila, D.: ‘Monocular pedestrian detection: survey and experiments’, IEEE Trans. Pattern Anal. Mach. Intell., 2009, 31, (12), pp. 21792195.
    17. 17)
      • 17. Li, T., Chang, H., Wang, M., et al: ‘Crowded scene analysis: a survey’, IEEE Trans. Circuits Syst. Video Technol., 2015, 25, (3), pp. 367386.
    18. 18)
      • 18. Paul, M., Haque, S.M.E., Chakraborty, S.: ‘Human detection in surveillance videos and its applications – a review’, EURASIP J. Adv. Signal Process., 2013, 2013, (1), pp. 176.
    19. 19)
      • 19. Zhou, H., Hu, H.: ‘Human motion tracking for rehabilitation: a survey’, Biomed. Signal Proc. Control, 2008, 3, (1), pp. 118.
    20. 20)
      • 20. Garća, G.M., Klein, D.A., Stückler, J., et al: ‘Adaptive multi-cue 3D tracking of arbitrary objects’, Pattern Recognit., 2012, 7476, pp. 357366.
    21. 21)
      • 21. Song, S., Xiao, J.: ‘Tracking revisited using RGBD camera: unified benchmark and baselines’. IEEE Conf. on Computer Vision, 2013, pp. 233240.
    22. 22)
      • 22. Wang, Q., Fang, J., Yuan, Y.: ‘Multi-cue based tracking’, Neurocomputing, 2014, 131, pp. 227236.
    23. 23)
      • 23. Zhong, B., Shen, Y., Chen, Y., et al: ‘Online learning 3D context for robust visual tracking’, Neurocomputing, 2015, 151, Part 2, pp. 710718.
    24. 24)
      • 24. Walk, S., Schindler, K., Schiele, B.: ‘Disparity statistics for pedestrian detection: combining appearance, motion and stereo’. European Conf. on Computer Vision, 2010, pp. 182195.
    25. 25)
      • 25. Spinello, L., Arras, K. O.: ‘People detection in RGB-D data’. 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA, 2011, pp. 38383843.
    26. 26)
      • 26. Wang, C., Liu, H., Ma, L.: ‘Depth Motion Detection–A Novel RS-Trigger Temporal Logic based Method’, IEEE Signal Process. Lett., 2014, 21, (6), pp. 717721.
    27. 27)
      • 27. Xia, L., Chen, C.-C., Aggarwal, J.: ‘Human detection using depth information by Kinect’. Computer Vision and Pattern Recognition Workshops, 2011, pp. 1522.
    28. 28)
      • 28. Stahlschmidt, C., Gavriilidis, A., Velten, J., et al: ‘Applications for a people detection and tracking algorithm using a time-of-flight camera’, Multimedia Tools Appl., 2016, 75, (17), pp. 1076910786.
    29. 29)
      • 29. Bagautdinov, T., Fleuret, F., Fua, P.: ‘Probability occupancy maps for occluded depth images’. IEEE Conf. on Computer Vision and Pattern Recognition, 2015, pp. 28292837.
    30. 30)
      • 30. Fosty, B., Crispim-Junior, C.F., Badie, J., et al: ‘Event recognition system for older people monitoring using an RGB-D camera’. Workshop on Assistance and Service Robotics in a Human Environment, 2013.
    31. 31)
      • 31. Dondi, P., Lombardi, L., Cinque, L.: ‘Multisubjects tracking by time-of-flight camera’. Conf. on Image Analysis and Processing, 2013, vol. 8156, pp. 692701.
    32. 32)
      • 32. Yun, K., Honorio, J., Chattopadhyay, D., et al: ‘Two-person interaction detection using body-pose features and multiple instance learning’. IEEE Conf. on Computer Vision and Pattern Recognition Workshops, 2012, pp. 2835.
    33. 33)
      • 33. Xu, N., Liu, A., Nie, W., et al: ‘Multi-modal & multi-view & interactive benchmark dataset for human action recognition’. ACM Conf. on Multimedia, 2015, pp. 11951198.
    34. 34)
      • 34. Shahroudy, A., Liu, J., Ng, T.-T., et al: ‘NTU RGB+D: a large scale dataset for 3D human activity analysis’, arXiv preprint arXiv:1604.02808.
    35. 35)
      • 35. Grenader, E., Gasques Rodrigues, D., Nos, F., et al: ‘The VideoMob interactive art installation connecting strangers through inclusive digital crowds’, ACM Trans. Inter. Intell. Syst., 2015, 5, (2), pp. 7:17:31.
    36. 36)
      • 36. Stone, E.E., Skubic, M.: ‘Fall detection in homes of older adults using the Microsoft Kinect’, IEEE J. Biomed. Health Inf., 2015, 19, (1), pp. 290301.
    37. 37)
      • 37. Zhu, N., Diethe, T., Camplani, M., et al: ‘Bridging e-health and the internet of things: the SPHERE project’, IEEE Intell. Syst., 2015, 30, (4), pp. 3946.
    38. 38)
      • 38. Dalal, N., Triggs, B.: ‘Histograms of oriented gradients for human detection’. IEEE Computer Vision and Pattern Recognition Conf., 2005, pp. 886893.
    39. 39)
      • 39. Bourdev, L., Malik, J.: ‘Poselets: body part detectors trained using 3D human pose annotations’. IEEE Int. Conf. on Computer Vision, 2009, pp. 13651372.
    40. 40)
      • 40. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., et al: ‘Object detection with discriminatively trained part based models’, IEEE Trans. Pattern Anal. Mach. Intell., 2010, 32, (9), pp. 16271645.
    41. 41)
      • 41. Viola, P., Jones, M.: ‘Robust real-time face detection’, Int. J. Comput. Vis., 2004, 57, (2), pp. 137154.
    42. 42)
      • 42. Bansal, M., Jung, S.-H., Matei, B., et al: ‘A real-time pedestrian detection system based on structure and appearance classification’. IEEE Int. Conf. on Robotics and Automation, 2010, pp. 903909.
    43. 43)
      • 43. Salas, J., Tomasi, C.: ‘People detection using color and depth images’. Mexican Conf. on Pattern Recognition, 2011, pp. 127135.
    44. 44)
      • 44. Dan, B.-K., Kim, Y.-S., Suryanto, J.-Y., et al: ‘Robust people counting system based on sensor fusion’, IEEE Trans. Consum. Electron., 2012, 58, (3), pp. 10131021.
    45. 45)
      • 45. Darrell, T., Gordon, G., Harville, M., et al: ‘Integrated person tracking using stereo, color, and pattern detection’, Int. J. Comput. Vis., 2000, 37, (2), pp. 175185.
    46. 46)
      • 46. Han, J., Pauwels, E.J., de Zeeuw, P.M., et al: ‘Employing a RGB-D sensor for real-time tracking of humans across multiple re-entries in a smart environment’, IEEE Trans. Consum. Electron., 2012, 58, (2), pp. 255263.
    47. 47)
      • 47. Bajracharya, M., Moghaddam, B., Howard, A., et al: ‘A fast stereo-based system for detecting and tracking pedestrians from a moving vehicle’, The Int. J. Robot. Res., 2009, 28, (11-12), pp. 14661485.
    48. 48)
      • 48. Zhang, H., Reardon, C., Parker, L.: ‘Real-time multiple human perception with color-depth cameras on a mobile robot’, IEEE Trans. Cybern., 2013, 43, (5), pp. 14291441.
    49. 49)
      • 49. Galamakis, G., Zabulis, X., Koutlemanis, P., et al: ‘Tracking persons using a network of RGBD cameras’. Int. Conf. on Pervasive Technologies for Assistive Environments, 2014, pp. 63:163:4.
    50. 50)
      • 50. Liu, J., Liu, Y., Cui, Y., et al: ‘Real-time human detection and tracking in complex environments using single RGB-D camera’. IEEE Int. Conf. on Image Processing, 2013, pp. 30883092.
    51. 51)
      • 51. Liu, J., Liu, Y., Zhang, G., et al: ‘Detecting and tracking people in real time with RGB-D camera’, Pattern Recognit. Lett., 2015, 53, pp. 1623.
    52. 52)
      • 52. Liu, J., Zhang, G., Liu, Y., et al: ‘An ultra-fast human detection method for color-depth camera’, J. Vis. Commun. Image Represent., 2015, 31, pp. 177185.
    53. 53)
      • 53. Luber, M., Spinello, L., Arras, K.O.: ‘People tracking in RGB-D data with on-line boosted target models’. Int. Conf. on Intelligent Robots and Systems, 2011, pp. 38443849.
    54. 54)
      • 54. Linder, T., Arras, K.O.: ‘Multi-model hypothesis tracking of groups of people in RGB-D data’. IEEE Conf. on Information Fusion, Salamanca, Spain, 2014, pp. 17.
    55. 55)
      • 55. Ess, A., Leibe, B., Schindler, K., et al: ‘Robust multiperson tracking from a mobile platform’, IEEE Trans. Pattern Anal. Mach. Intell., 2009, 31, (10), pp. 18311846.
    56. 56)
      • 56. Jafari, O., Mitzel, D., Leibe, B.: ‘Real-time RGB-D based people detection and tracking for mobile robots and head-worn cameras’. IEEE Conf. on Robotics and Automation, 2014, pp. 56365643.
    57. 57)
      • 57. Muñoz Salinas, R., Aguirre, E., Garća-Silvente, M.: ‘People detection and tracking using stereo vision and color’, Image Vis. Comput., 2007, 25, (6), pp. 9951007.
    58. 58)
      • 58. Munaro, M., Basso, F., Menegatti, E.: ‘Tracking people within groups with RGB-D data’. IEEE/RSJ Conf. on Intelligent Robots and Systems, 2012, pp. 21012107.
    59. 59)
      • 59. Munaro, M., Lewis, C., Chambers, D., et al: ‘RGB-D human detection and tracking for industrial environments’. Int. Conf. on Intelligent Autonomous Systems, 2014, pp. 16551668.
    60. 60)
      • 60. Almazán, E., Jones, G.: ‘A depth-based polar coordinate system for people segmentation and tracking with multiple RGB-D sensors’. IEEE ISMAR Workshop on Tracking Methods and Applications, 2014.
    61. 61)
      • 61. Almazán, E., Jones, G.: ‘Tracking people across multiple non-overlapping RGB-D sensors’. IEEE Conf. on Computer Vision and Pattern Recognition Workshops, 2013, pp. 831837.
    62. 62)
      • 62. Bahadori, S., Iocchi, L., Leone, G., et al: ‘Real-time people localization and tracking through fixed stereo vision’. Innovations in Applied Artificial Intelligence, 2005, pp. 4454.
    63. 63)
      • 63. Beymer, D., Konolige, K.: ‘Real-time tracking of multiple people using stereo’. IEEE Conf. on Computer Vision Workshops, 1999, pp. 10761083.
    64. 64)
      • 64. Satake, J., Chiba, M., Miura, J.: ‘Visual person identification using a distance-dependent appearance model for a person following robot’, Int. J. Autom. Comput., 2013, 10, (5), pp. 438446.
    65. 65)
      • 65. Vo, D.M., Jiang, L., Zell, A.: ‘Real time person detection and tracking by mobile robots using RGB-D images’. IEEE Conf. on Robotics and Biomimetics, 2014, pp. 689694.
    66. 66)
      • 66. Vo, D.M., Masselli, A., Zell, A.: ‘Real time face detection using geometric constraints, navigation and depth-based skin segmentation on mobile robots’. IEEE Symp. on Robotic and Sensors Environments, 2012, pp. 180185.
    67. 67)
      • 67. Harville, M.: ‘Stereo person tracking with adaptive plan-view templates of height and occupancy statistics’, Image Vis. Comput., 2004, 22, (2), pp. 127142.
    68. 68)
      • 68. Muñoz Salinas, R.: ‘A Bayesian plan-view map based approach for multiple-person detection and tracking’, Pattern Recogn., 2008, 41, (12), pp. 36653676.
    69. 69)
      • 69. Muñoz Salinas, R., Medina-Carnicer, R., Madrid-Cuevas, F.: ‘A. Carmona-Poyato, People detection and tracking with multiple stereo cameras using particle filters’, J. Vis. Commun. Image Represent., 2009, 20, (5), pp. 339350.
    70. 70)
      • 70. Muñoz Salinas, R., Garća-Silvente, M., Carnicer, R.M.: ‘Adaptive multi-modal stereo people tracking without background modelling’, J. Vis. Commun. Image Represent., 2008, 19, (2), pp. 7591.
    71. 71)
      • 71. Choi, W., Pantofaru, C., Savarese, S.: ‘Detecting and tracking people using an RGB-D camera via multiple detector fusion’. IEEE Conf. on Computer Vision Workshops, 2011, pp. 10761083.
    72. 72)
      • 72. Choi, W., Pantofaru, C., Savarese, S.: ‘A general framework for tracking multiple people from a moving camera’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (7), pp. 15771591.
    73. 73)
      • 73. Migniot, C., Ababsa, F.: ‘Hybrid 3D–2D human tracking in a top view’, J. Real-Time Image Process., 2016, 11, (4), pp. 769784.
    74. 74)
      • 74. Gao, S., Han, Z., Li, C., et al: ‘Real-time multipedestrian tracking in traffic scenes via an RGB-D-based layered graph model’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (5), pp. 28142825.
    75. 75)
      • 75. Ma, A.J., Yuen, P.C., Saria, S.: ‘Deformable distributed multiple detector fusion for multi-person tracking’, arXiv preprint arXiv:1512.05990, 2015.
    76. 76)
      • 76. Rubner, Y., Tomasi, C., Guibas, L.: ‘The earth mover's distance as a metric for image retrieval’, J. Int. Comput. Vis., 2000, 40, (2), pp. 99121.
    77. 77)
      • 77. Argyros, A.A., Lourakis, M.I.: ‘Real-time tracking of multiple skin-colored objects with a possibly moving camera’. European Conf. on Computer Vision, 2004, pp. 368379.
    78. 78)
      • 78. Padeleris, P., Zabulis, X., Argyros, A.: ‘Multicamera tracking of multiple humans based on colored visual hulls’. IEEE Conf. on Emerging Technologies Factory Automation, 2013, pp. 18.
    79. 79)
      • 79. Cox, I.J., Hingorani, S.L.: ‘An efficient implementation of Reid's multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking’, IEEE Trans. Pattern Anal. Mach. Intell., 1996, 18, (2), pp. 138150.
    80. 80)
      • 80. Eveland, C., Konolige, K., Bolles, R.: ‘Background modeling for segmentation of video-rate stereo sequences’. IEEE Computer Vision and Pattern Recognition, 1998, pp. 266271.
    81. 81)
      • 81. Leibe, B., Schindler, K., Cornelis, N., et al: ‘Coupled object detection and tracking from static cameras and moving vehicles’, IEEE Trans. Pattern Anal. Mach. Intell., 2008, 30, (10), pp. 16831698.
    82. 82)
      • 82. Sudowe, P., Leibe, B.: ‘Efficient use of geometric constraints for sliding-window object detection in video’. Computer Vision Systems, 2011, pp. 1120.
    83. 83)
      • 83. Satake, J., Miura, J.: ‘Robust stereo-based person detection and tracking for a person following robot’. ICRA Workshop on People Detection and Tracking, 2009, pp. 110.
    84. 84)
      • 84. Lowe, D.: ‘Object recognition from local scale-invariant features’. IEEE Conf. on Computer Vision, 1999, pp. 11501157.
    85. 85)
      • 85. Kuhn, H.: ‘The Hungarian method for the assignment problem’, Naval Res. Logist. Q., 1955, 2, pp. 8397.
    86. 86)
      • 86. Harville, M., Gordon, G., Woodfill, J.: ‘Foreground segmentation using adaptive mixture models in color and depth’. IEEE Workshop on Detection and Recognition of Events in Video, 2001, pp. 311.
    87. 87)
      • 87. Milan, A., Schindler, K., Roth, S.: ‘Multi-target tracking by discrete-continuous energy minimization’, IEEE Trans. Pattern Anal. Mach. Intell., 2016, 38, (10), pp. 20542068.
    88. 88)
      • 88. Comaniciu, D., Meer, P.: ‘Mean shift: a robust approach toward feature space analysis’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (5), pp. 603619.
    89. 89)
      • 89. Chambers, D.R., Flannigan, C., Wheeler, B.: ‘High-accuracy real-time pedestrian detection system using 2D and 3D features’. SPIE Defense, Security, and Sensing, 2012, vol. 8384, pp. 83840G83840G–11.
    90. 90)
      • 90. Fischler, M.A., Bolles, R.C.: ‘Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography’, Commun. ACM, 1981, 24, (6), pp. 381395.
    91. 91)
      • 91. Vedaldi, A., Soatto, S.: ‘Quick Shift and kernel methods for mode seeking’. European Conf. on Computer Vision, 2008, pp. 705718.
    92. 92)
      • 92. Camplani, M., Salgado, L.: ‘Background foreground segmentation with RGB-D Kinect data: an efficient combination of classifiers’, J. Vis. Commun. Image Represent., 2014, 25, (1), pp. 122136.
    93. 93)
      • 93. Camplani, M., del Blanco, C.R., Salgado, L., et al: ‘Advanced background modeling with RGB-D sensors through classifiers combination and inter-frame foreground prediction’, Mach. Vis. Appl., 2014, 25, (5), pp. 11971210.
    94. 94)
      • 94. Gordon, G., Darrell, T., Woodfill, J.: ‘Background estimation and removal based on range and color’. IEEE Conf. on Computer Vision and Pattern Recognition, 1999.
    95. 95)
      • 95. Kammerl, J.: ‘Octree Point Cloud Compression in PCL’, 2011.
    96. 96)
      • 96. Ganotra, D., Joseph, J., Singh, K.: ‘Modified geometry of ring-wedge detector for sampling Fourier transform of fingerprints for classification using neural networks’, Opt. Lasers Eng., 2004, 42, (2), pp. 167177.
    97. 97)
      • 97. Quigley, M., Conley, K., Gerkey, B.P., et al: ‘ROS: an open-source robot operating system’. ICRA Workshop on Open Source Software, 2009.
    98. 98)
      • 98. Munaro, M., Horn, A., Illum, R., et al: ‘OpenPTrack: people tracking for heterogeneous networks of color-depth cameras’. IAS Workshop on 3D Robot Perception with Point Cloud Library, 2014, pp. 235247.
    99. 99)
      • 99. Munaro, M., Basso, F., Menegatti, E.: ‘OpenPTrack: open source multi-camera calibration and people tracking for RGB-D camera networks’, Robot. Auton. Syst., 2016, 75, Part B, pp. 525538.
    100. 100)
      • 100. Ess, A., Leibe, B., Schindler, K., et al: ‘A mobile vision system for robust multi-person tracking’. IEEE Conf. on Computer Vision and Pattern Recognition, 2008, pp. 18.
    101. 101)
      • 101. Munaro, M., Menegatti, E.: ‘Fast RGB-D people tracking for service robots’, Auton. Robots, 2014, 37, (3), pp. 227242.
    102. 102)
      • 102. Felzenszwalb, P.F., Huttenlocher, D.P.: ‘Efficient belief propagation for early vision’, Int. J. Comput. Vis., 2006, 70, (1), pp. 4154.
    103. 103)
      • 103. Leal-Taixé, L., Milan, A., Reid, I., et al: ‘MOTChallenge 2015: Towards a Benchmark for Multi-Target Tracking’, arXiv:1504.01942 [cs]ArXiv: 1504.01942.
    104. 104)
      • 104. Rusu, R.B., Cousins, S.: ‘3D is here: Point Cloud Library (PCL)’. IEEE Int. Conf. on Robotics and Automation, 2011, pp. 14.
    105. 105)
      • 105. Mitzel, D., Leibe, B.: ‘Close-range human detection and tracking for head-mounted cameras’. British Machine Vision Conf., 2012, pp. 8.18.11.
    106. 106)
      • 106. Bernardin, K., Stiefelhagen, R.: ‘Evaluating multiple object tracking performance: the CLEAR MOT metrics’, J. Image Video Process., 2008, 2008, pp. 1:11:10.
    107. 107)
      • 107. Szczodrak, M., Dalka, P., Czyzewski, A.: ‘Performance evaluation of video object tracking algorithm in autonomous surveillance system’. Int. Conf. on Information Technology, 2010, pp. 3134.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cvi.2016.0178
Loading

Related content

content/journals/10.1049/iet-cvi.2016.0178
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address