Abstract
For many years articulated tracking has been an active research topic in the computer vision community. While working solutions have been suggested, computational time is still problematic. We present a GPU implementation of a ray-casting based likelihood model that is orders of magnitude faster than a traditional CPU implementation. We explain the non-intuitive steps required to attain an optimized GPU implementation, where the dominant part is to hide the memory latency effectively. Benchmarks show that computations which previously required several minutes, are now performed in few seconds.
Keywords
Download to read the full chapter text
Chapter PDF
References
Poppe, R.: Vision-based human motion analysis: An overview. Computer Vision and Image Understanding 108, 4–18 (2007)
Cappé, O., Godsill, S.J., Moulines, E.: An overview of existing methods and recent advances in sequential Monte Carlo. Proceedings of the IEEE 95, 899–924 (2007)
Sminchisescu, C., Triggs, B.: Kinematic Jump Processes for Monocular 3D Human Tracking. In: IEEE International Conference on Computer Vision and Pattern Recognition, pp. 69–76 (2003)
Deutscher, J., Blake, A., Reid, I.: Articulated body motion capture by annealed particle filtering. In: CVPR, p. 2126. IEEE Computer Society (2000)
Hauberg, S., Sommer, S., Pedersen, K.S.: Gaussian-Like Spatial Priors for Articulated Tracking. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part I. LNCS, vol. 6311, pp. 425–437. Springer, Heidelberg (2010)
Bandouch, J., Beetz, M.: Tracking Humans Interacting with the Environment Using Efficient Hierarchical Sampling and Layered Observation Models. In: IEEE Int. Workshop on Human-Computer Interaction (HCI) (2009)
Cabido, R., Concha, D., Pantrigo, J.J., Montemayor, A.S.: High Speed Articulated Object Tracking Using GPUs: A Particle Filter Approach. In: 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks, pp. 757–762. IEEE (2009)
Rohr, K.: Towards model-based recognition of human movements in image sequences. CVGIP-Image Understanding 59, 94–115 (1994)
Sidenbladh, H., Black, M.J., Fleet, D.J.: Stochastic Tracking of 3D Human Figures Using 2D Image Motion. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1843, pp. 702–718. Springer, Heidelberg (2000)
Sengupta, S., Harris, M., Zhang, Y., Owens, J.D.: Scan primitives for gpu computing. In: Proceedings of the 22nd ACM SIGGRAPH/EUROGRAPHICS Symposium on Graphics Hardware, Aire-la-Ville, Switzerland, pp. 97–106. Eurographics Association (2007)
CUDPP: Cuda data parallel primitives library, http://code.google.com/p/cudpp/ (accessed Online April 2010)
NVIDIA Corporation: NVIDIA CUDA Best Practices Guide. version 3.0 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Friborg, R.M., Hauberg, S., Erleben, K. (2012). GPU Accelerated Likelihoods for Stereo-Based Articulated Tracking. In: Kutulakos, K.N. (eds) Trends and Topics in Computer Vision. ECCV 2010. Lecture Notes in Computer Science, vol 6554. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35740-4_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-35740-4_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35739-8
Online ISBN: 978-3-642-35740-4
eBook Packages: Computer ScienceComputer Science (R0)