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View all- Carletti VGreco ASaggese AVento M(2019)An effective real time gender recognition system for smart camerasJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-019-01267-5Online publication date: 15-Mar-2019
Foreground Detection Optimization for SoCs embedded on Smart Cameras
Article No.: 31, Pages 1 - 5
Abstract
In this paper we study the effectiveness of a set of optimizations applied on a foreground detection and background maintainance algorithm. The optimizations were specifically devised to run in real time on hardware architectures embedded on commercial smart cameras. In order to achieve these aims we focused our attention on two kinds of optimizations based on the elimination of floating-point operations and the adoption of SIMD instructions. The optimized version of the algorithm has been tested on two RISC architectures (CRISv32 and MIPS 32Kc) considering different stream resolutions. The results confirm the effectiveness of the proposed solutions, which allows to process in real-time up to VGA resolution.
References
[1]
D. Bouris, A. Nikitakis, and I. Papaefstathiou, "Fast and Efficient FPGA-based Feature Detection Employing the SURF Algorithm," in Field-Programmable Custom Computing Machines (FCCM), 2010 18th IEEE Annual International Symposium on, May 2010, pp. 3--10.
[2]
M. Schaeferling and G. Kiefer, "Object Recognition on a Chip: A Complete SURF-Based System on a Single FPGA," in Reconfigurable Computing and FPGAs (ReConFig), 2011 International Conference on, Nov 2011, pp. 49--54.
[3]
H. Bay, T. Tuytelaars, and L. Gool, "Surf: Speeded up robust features," in Computer Vision âĂŞ ECCV 2006, ser. Lecture Notes in Computer Science, A. Leonardis, H. Bischof, and A. Pinz, Eds. Springer Berlin Heidelberg, 2006, vol. 3951, pp. 404--417.
[4]
K. Pauwels, M. Tomasi, J. Diaz Alonso, E. Ros, and M. Van Hulle, "A Comparison of FPGA and GPU for Real-Time Phase-Based Optical Flow, Stereo, and Local Image Features," Computers, IEEE Transactions on, vol. 61, no. 7, pp. 999--1012, July 2012.
[5]
S. K. Teoh, V. V. Yap, C. S. Soh, and P. Sebastian, "Implementation and Optimization of Human Tracking System using ARM Embedded Platform," in Intelligent and Advanced Systems (ICIAS), 2012 4th International Conference on, vol. 1, June 2012, pp. 353--356.
[6]
G. Dedeoglu, B. Kisacanin, D. Moore, V. Sharma, and A. Miller, "An Optimized Vision Library Approach for Embedded Systems," in Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on, June 2011, pp. 8--13.
[7]
M. Shoaib, T. Elbrandt, E. Zaretskiy, and J. Ostermann, "Hierarchical Bayer-pattern Based Background Subtraction for Low Resource Devices," in Circuits and Systems (ISCAS), 2012 IEEE International Symposium on, May 2012, pp. 1867--1870.
[8]
J. K. Suhr, H. G. Jung, G. Li, and J. Kim, "Mixture of Gaussians-based Background Subtraction for Bayer-pattern Image Sequences," Circuits and Systems for Video Technology, IEEE Transactions on, vol. 21, no. 3, pp. 365--370, March 2011.
[9]
Y. Yoo and T.-S. Park, "A Moving Object Detection Algorithm for Smart Cameras," in Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08. IEEE Computer Society Conference on, June 2008, pp. 1--8.
[10]
S. Apewokin, B. Valentine, R. Bales, L. Wills, and S. Wills, "Tracking Multiple Pedestrians in Real-Time using Kinematics," in Computer Vision and Pattern Recognition Workshops, 2008. CVPRW '08. IEEE Computer Society Conference on, June 2008, pp. 1--6.
[11]
R. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, "Image Change Detection Algorithms: a Systematic Survey," Image Processing, IEEE Transactions on, vol. 14, no. 3, pp. 294--307, March 2005.
[12]
S. Gupte, O. Masoud, R. Martin, and N. Papanikolopoulos, "Detection and Classification of Vehicles," Intelligent Transportation Systems, IEEE Transactions on, vol. 3, no. 1, pp. 37--47, Mar 2002.
[13]
D. Conte, P. Foggia, M. Petretta, F. Tufano, and M. Vento, "Meeting the Application Requirements of Intelligent Video Surveillance Systems in Moving Object Detection," in Pattern Recognition and Image Analysis, ser. Lecture Notes in Computer Science, S. Singh, M. Singh, C. Apte, and P. Perner, Eds. Springer Berlin Heidelberg, 2005, vol. 3687, pp. 653--662.
[14]
(2014) Axis camera application platform reference web page. {Online}. Available: http://www.axis.com/techsup/cam_servers/dev/application_platform/index.htm
[15]
(2013, Feb.) Rapp library official web page with documentation and benchmarks. {Online}. Available: http://savannah.nongnu.org/projects/rapp/
Index Terms
- Foreground Detection Optimization for SoCs embedded on Smart Cameras
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Published: 04 November 2014
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ICDSC '14
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ICDSC '14: International Conference on Distributed Smart Cameras
November 4 - 7, 2014
Venezia Mestre, Italy
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ICDSC '14 Paper Acceptance Rate 49 of 69 submissions, 71%;
Overall Acceptance Rate 92 of 117 submissions, 79%
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View all- Carletti VGreco ASaggese AVento M(2019)An effective real time gender recognition system for smart camerasJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-019-01267-5Online publication date: 15-Mar-2019
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