Abstract
Active Shape Model has been proven to be one of the most popular methods for recognizing non-rigid objects, which requires huge computation power for real time people tracking. After analyzing the parallel characteristics of the algorithm, we propose a deep pipelined structure for accelerating the Active Shape Model algorithm. The computing engine is organized into a deep pipeline network composing of multiple floating-point arithmetic units, including adders, multipliers, dividers and SQRT etc. A linear multiplication-accumulation (MAC) unit is designed to lower the complexity of the computing resources while keeping high pipeline throughput. In the optimization of the memory efficiency for loading random data in large images during the step of local search, we propose an on-chip buffer scheme to eliminate random accesses to off-chip memory. Experimental results show that our FPGA implementation achieves over 15 times of speedup compared with the sequentially-implemented software solution in Pentium 4 computer.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kass, M., Witkins, A., Terzopoulos, D.: Snakes: Active contour models. In: Proc. First International Conference on Comuputer Vision, pp. 259–268 (1987)
Wiskott, L., Fellous, J., Kruger, N., der Malsburg, C.V.: Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Machine Intell. 19, 775–779 (1997)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models–their training and application. Computer Vision and Image Understanding 61, 38–59 (1995)
Baumberg, A., Hogg, D.: An efficient method for contour tracking using active shape models. In: Proc. IEEE Workshop on Motion of Non-Rigid and Articulated Objects, pp. 194–199 (1994)
Li, Y., Lai, J.H., Yuen, P.C.: Multi-template asm method for feature points detection of facial image with diverse expressions. In: Proc. 7th International Conference on Automatic Face and Gesture Recognition, pp. 435–440 (2006)
Zhao, Z., Teoh, E.K.: A novel 3D statistical shape model for segmentation of medical images. In: Proc. of the 2006 International Symposium on Visual Computing, pp. 638–647 (2006)
Rogers, M., Graham, J.: Robust active shape model search. In: Proc. 7th European Conference on Computer Vision, pp. 517–530 (2002)
Baumberg, A.: Leaning deformable models for tracking human motion. Ph.D. dissertation. Univ. of Leeds, Leeds (1995)
Blake, A., Curwen, R., Zisserman, A.: A framework of spatio-temporal control in the tracking of visual contours. International Journal of Computer Vision 11, 127–145 (1993)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dou, Y., Xu, J. (2007). FPGA-Accelerated Active Shape Model for Real-Time People Tracking. In: Choi, L., Paek, Y., Cho, S. (eds) Advances in Computer Systems Architecture. ACSAC 2007. Lecture Notes in Computer Science, vol 4697. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74309-5_26
Download citation
DOI: https://doi.org/10.1007/978-3-540-74309-5_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74308-8
Online ISBN: 978-3-540-74309-5
eBook Packages: Computer ScienceComputer Science (R0)