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capable of real-time computation at 1.7 ms per frame on average. The next steps would be to combine the real-time object tracking approach with human tracking and extend the framework towards human activity recognition in industrial settings. 7. Acknowledgment This research is funded by the projects KoMoProd (Austrian Bundesministerium für Verkehr, Innovation und Technologie), SIAM (FFG, 849971) and CompleteMe (FFG, 849441). 8. References [1] Armando Pesenti Gritti et al., “Kinect-based people detection and tracking from small-footprint ground robots”, in Proc. IEEE Int. Conf. on Intelligent Robots and Systems, 2014. [2] A. Yilmaz et. al., “Object tracking: A survey,” ACM Computer Surveys (CSUR), vol. 38, no. 4, pp. 1–45, 2006. [3] B. Drost, M. Ulrich, N. Navab, and S. Ilic, “Model globally, match locally: Efficient and robust 3d object recognition”, in Proc. Of IEEE Int. Conf. on Computer Vision and Pattern Recognition, 2010. [4] C. Choi et al., “Robust 3D visual tracking using particle filtering on the special Euclidean group: A combined approach of keypoint and edge features”, Int. Journal of Robotics Research, vol. 31, no.4, pp. 498–519, 2012. [5] C.Choi and HI. Christensen, “RGB-D Object Tracking: A Particle Filter Approach on GPU”, in Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 1084-1091, 2013. [6] C. Papazov and D. Burschka, “An efficient RANSAC for 3-D object recognition in noisy and occluded scenes”, in Proc. 10th Asian Conf. Computer Vision (ACCV), 2010, pp. 135–148. [7] C. Ren, V. Prisacariu, D. Murray, and I. Reid, “Star3d: Simultaneous tracking and reconstruction of 3d objects using rgb-d data,” in Proc. Int. Conf. on Computer Vision, 2013. [8] J. Suarez and R.R. Murphy, “Hand gesture recognition with depth images: A review,” in IEEE Int. Symposium on Robot and Human Interactive Communication, pp. 411–417, 2012. [9] Krull Alexander et al., "6-dof model based tracking via object coordinate regression", Computer Vision—ACCV, 2014, Springer International Publishing, pp 384-399, 2015. [10] M. Isard and A. Blake, “Condensation - Conditional density propagation for visual tracking,” Int. Journal of Computer Vision, vol. 29, no. 1, 1998. [11] Mao Ye et al., “A survey on human motion analysis from depth data”, in Timeof- Flight and Depth Imaging. Sensors, Algorithms, and Applications, pp. 149–187, Springer, 2013. [12] S. Akkaladevi, M. Ankerl et al., “Tracking multiple rigid symmetric and non-symmetric objects in real-time using depth data,” [to appear] in Proc. IEEE International Conference on Robotics and Automation (ICRA), 2016. [13] S. Koo, D. Lee, and D. Kwon, “Multiple object tracking using an rgb-d camera by hierarchical spatiotemporal data association”, in Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 1113–1118, 2013. [14] S. Koo, D. Lee, and D. Kwon, “Incremental object learning and robust tracking of multiple objects from rgb-d point set data,” Journal of Visual Communication and Image Representation, vol. 25, no. 1, pp. 108–121, 2014. [15] S. Koo, D. Lee, and D. Kwon, “Unsupervised object individuation from rgb-d image sequences”, in Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 4450– 4457, 2014. [16] S. Rusinkiewicz and M. Levoy, “Efficient variants of the icp algorithm”, in Proc. IEEE Int. Conf. on 3-D Digital Imaging and Modeling, pp. 145–152, 2001. [17] S. Winkelbach, S. Molkenstruck, F. M. Wahl, “Low-Cost Laser Range Scanner and Fast Surface Registration Approach”, Pattern Recognition (DAGM 2006) LNCS 4174, pp. 718-728, Springer 2006. [18] Rusu, R. B. Rusu and S. Cousins. “3d is here: Point cloud library (pcl)”, in Proc. IEEE International Conference on Robotics and Automation (ICRA), pp. 1- 4, 2011. [19] Tan David Joseph, and Slobodan Ilic, “Multi-forest Tracker: A Chameleon in Tracking”, in Proc. Of IEEE Int. Conf. on Computer Vision and Pattern Recognition, 2014. [20] Tan David Joseph et al., “A Versatile Learning-based 3D Temporal Tracker: Scalable, Robust, Online” in Proc. IEEE Int. Conf. on Computer Vision, vol. 1, No. 4, 2015. [21] X. Li, W. Hu, C. Shen, Z. Zhang, A. Dick, A. Hengel, “A survey of appearance models in visual object tracking”, ACM Transactions on Intelligent Systems and Technology, vol. 4, no. 4, pp. 1-48, 2013. 104
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Proceedings OAGM & ARW Joint Workshop 2016 on "Computer Vision and Robotics“
Title
Proceedings
Subtitle
OAGM & ARW Joint Workshop 2016 on "Computer Vision and Robotics“
Authors
Peter M. Roth
Kurt Niel
Publisher
Verlag der Technischen Universität Graz
Location
Wels
Date
2017
Language
English
License
CC BY 4.0
ISBN
978-3-85125-527-0
Size
21.0 x 29.7 cm
Pages
248
Keywords
Tagungsband
Categories
International
Tagungsbände

Table of contents

  1. Learning / Recognition 24
  2. Signal & Image Processing / Filters 43
  3. Geometry / Sensor Fusion 45
  4. Tracking / Detection 85
  5. Vision for Robotics I 95
  6. Vision for Robotics II 127
  7. Poster OAGM & ARW 167
  8. Task Planning 191
  9. Robotic Arm 207
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