Vision-Based SLAM and Moving Objects Tracking for the Perceptual Support of a Smart Walker Platform

详细信息    查看全文

• 作者：Paschalis Panteleris (16)
  Antonis A. Argyros (16) (17)
  
  16. Institute of Computer Science (ICS) ; Foundation for Research and Technology - Hellas (FORTH) ; N. Plastira 100 ; Vassilika Vouton ; 70013 ; Heraklion ; Crete ; Greece
  17. Department of Computer Science ; University of Crete ; Heraklion ; Greece
  
• 关键词：Visual tracking ; Human detection and tracking ; Mapping ; Egomotion estimation ; SLAMMOT ; Smart walker
• 刊名：Lecture Notes in Computer Science
• 出版年：2015
• 出版时间：2015
• 年：2015
• 卷：8927
• 期：1
• 页码：407-423
• 全文大小：1,638 KB
• 参考文献：1. Agrawal, M., Konolige, K., Iocchi, L.: Real-time detection of independent motion using stereo. In: IEEE WACV/MOTIONS 2005, vol. 2, pp. 207鈥?14 (2005)
  2. Angeline, PJ Evolutionary optimization versus particle swarm optimization: philosophy and performance differences. In: Porto, VW, Saravanan, N, Waagen, D, Eiben, AE eds. (1998) Evolutionary Programming VII. Springer, Heidelberg, pp. 601-610 CrossRef
  3. Argyros, AA, Lourakis, MIA Real-time tracking of multiple skin-colored objects with a possibly moving camera. In: Pajdla, T, Matas, JG eds. (2004) Computer Vision - ECCV 2004. Springer, Heidelberg, pp. 368-379 CrossRef
  4. Bibby, C, Reid, I Robust real-time visual tracking using pixel-wise posteriors. In: Forsyth, D, Torr, P, Zisserman, A eds. (2008) Computer Vision 鈥?ECCV 2008. Springer, Heidelberg, pp. 831-844 CrossRef
  5. Wang, C.C., Thorpe, C., Hebert, M., Thrun, S., Durrant-Whyte, H.: Simultaneous localization, mapping and moving object tracking. International Journal of Robotics Research (2004)
  6. Choi, W., Pantofaru, C., Savarese, S.: Detecting and tracking people using an rgb-d camera via multiple detector fusion. In: ICCV Workshops, pp. 1076鈥?083. IEEE (2011)
  7. Choi, W, Pantofaru, C, Savarese, S (2013) A general framework for tracking multiple people from a moving camera. IEEE Trans. Pattern Anal. Mach. Intell. 35: pp. 1577-1591 CrossRef
  8. Clerc, M, Kennedy, J (2002) The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6: pp. 58-73 CrossRef
  9. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, USA (2005)
  10. Endres, F., Hess, J., Sturm, J., Cremers, D., Burgard, W.: 3d mapping with an RGB-D camera. IEEE Transactions on Robotics (T-RO) (2013)
  11. Ess, A., Leibe, B., Schindler, K., Van Gool, L.J.: A mobile vision system for robust multi-person tracking. In: CVPR. IEEE Computer Society (2008)
  12. Ess, A, Leibe, B, Schindler, K, Gool, LJ (2009) Robust multiperson tracking from a mobile platform. IEEE Trans. Pattern Anal. Mach. Intell. 31: pp. 1831-1846 CrossRef
  13. Felzenszwalb, PF, Girshick, RB, McAllester, DA, Ramanan, D (2010) Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32: pp. 1627-1645 CrossRef
  14. Ferrari, V., Marin-Jimenez, M., Zisserman, A.: Progressive search space reduction for human pose estimation. In: Proceedings of the IEEE Computer Vision and Pattern Recognition, Alaska (2008)
  15. Fuentes-Pacheco, J., Ruiz-Ascencio, J., Rendn-Mancha, J.M.: Visual simultaneous localization and mapping: a survey. Artificial Intelligence Review, 1鈥?7 (2012)
  16. Gate, G., Breheret, A., Nashashibi, F.: Fast pedestrian detection in dense environment with a laser scanner and a camera. In: IEEE 69th Vehicular Technology Conference, VTC Spring 2009, pp. 1鈥? (2009)
  17. Henry, P, Krainin, M, Herbst, E, Ren, X, Fox, D (2012) Rgb-d mapping: Using kinect-style depth cameras for dense 3d modeling of indoor environments. Int. J. Rob. Res. 31: pp. 647-663 CrossRef
  18. 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. In: Proc. UIST, pp. 559鈥?68 (2011)
  19. Kennedy, J., Eberhart, R.C., Shi, Y.: Swarm intelligence. Morgan Kaufmann Publishers (2001)
  20. Luber, M., Spinello, L., Arras, K.O.: People tracking in rgb-d data with on-line boosted target models. In: Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), San Francisco, USA (2011)
  21. M谩rquez-G谩mez, D, Devy, M Active visual-based detection and tracking of moving objects from clustering and classification methods. In: Blanc-Talon, J, Philips, W, Popescu, D, Scheunders, P, Zem膷铆k, P eds. (2012) Advanced Concepts for Intelligent Vision Systems. Springer, Heidelberg, pp. 361-373 CrossRef
  22. Oikonomidis, I., Kyriazis, N., Argyros, A.A.: Tracking the articulated motion of two strongly interacting hands. In: CVPR. IEEE, June 2012
  23. Padeleris, P., Zabulis, X., Argyros, A.A.: Head pose estimation on depth data based on particle swarm optimization. In: IEEE CVPRW - HAU3D 2012 (2012)
  24. Ramanan, D, Forsyth, DA, Zisserman, A (2007) Tracking people by learning their appearance. IEEE Trans. Pattern Anal. Mach. Intell. 29: pp. 65-81 CrossRef
  25. Rosten, E, Drummond, T Machine learning for high-speed corner detection. In: Leonardis, A, Bischof, H, Pinz, A eds. (2006) Computer Vision 鈥?ECCV 2006. Springer, Heidelberg, pp. 430-443 CrossRef
  26. Smisek, J., Jancosek, M., Pajdla, T.: 3d with kinect. In: ICCV Workshops, pp. 1154鈥?160. IEEE (2011)
  27. Sol脿, J.: Towards Visual Localization, Mapping and Moving Objects Tracking by a Mobile Robot: a Geometric and Probabilistic Approach. PhD thesis, Institut National Polytechnique de Toulouse (2007)
  28. Viola, P., Jones, M., Snow, D.: Detecting pedestrians using patterns of motion and appearance. In: Proceedings of the 9th IEEE International Conference on Computer Vision, Nice, France (2003)
  29. Wangsiripitak, S., Murray, D.W.: Avoiding moving outliers in visual slam by tracking moving objects. In: IEEE ICRA 2009, pp. 705鈥?10. IEEE Press, Piscataway (2009)
  30. Yilmaz, A., Javed, O., Shah, M.: Object tracking: A survey. ACM Comput. Surv. 38(4) (2006)
  31. Zhao, T., Nevatia, R.: Tracking multiple humans in crowded environment. In: CVPR(2), pp. 406鈥?13 (2004)
  
• 作者单位：Computer Vision - ECCV 2014 Workshops
• 丛书名：978-3-319-16198-3
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

The problems of vision-based detection and tracking of independently moving objects, localization and map construction are highly interrelated, in the sense that the solution of any of them provides valuable information to the solution of the others. In this paper, rather than trying to solve each of them in isolation, we propose a method that treats all of them simultaneously. More specifically, given visual input acquired by a moving RGBD camera, the method detects independently moving objects and tracks them in time. Additionally, the method estimates the camera (ego)motion and the motion of the tracked objects in a coordinate system that is attached to the static environment, a map of which is progressively built from scratch. The loose assumptions that the method adopts with respect to the problem parameters make it a valuable component for any robotic platform that moves in a dynamic environment and requires simultaneous tracking of moving objects, egomotion estimation and map construction. The usability of the method is further enhanced by its robustness and its low computational requirements that permit real time execution even on low-end CPUs.