Page - 104 - in Proceedings - OAGM & ARW Joint Workshop 2016 on "Computer Vision and Robotics“
Image of the Page - 104 -
Text of the Page - 104 -
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
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