Browse > Article
http://dx.doi.org/10.5573/ieek.2013.50.5.083

Hardware Design of SURF-based Feature extraction and description for Object Tracking  

Do, Yong-Sig (Department of electronics and communication engineering, Kwangwoon University)
Jeong, Yong-Jin (Department of electronics and communication engineering, Kwangwoon University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.50, no.5, 2013 , pp. 83-93 More about this Journal
Abstract
Recently, the SURF algorithm, which is conjugated for object tracking system as part of many computer vision applications, is a well-known scale- and rotation-invariant feature detection algorithm. The SURF, due to its high computational complexity, there is essential to develop a hardware accelerator in order to be used on an IP in embedded environment. However, the SURF requires a huge local memory, causing many problems that increase the chip size and decrease the value of IP in ASIC and SoC system design. In this paper, we proposed a way to design a SURF algorithm in hardware with greatly reduced local memory by partitioning the algorithms into several Sub-IPs using external memory and a DMA. To justify validity of the proposed method, we developed an example of simplified object tracking algorithm. The execution speed of the hardware IP was about 31 frame/sec, the logic size was about 74Kgate in the 30nm technology with 81Kbytes local memory in the embedded system platform consisting of ARM Cortex-M0 processor, AMBA bus(AHB-lite and APB), DMA and a SDRAM controller. Hence, it can be used to the hardware IP of SoC Chip. If the image processing algorithm akin to SURF is applied to the method proposed in this paper, it is expected that it can implement an efficient hardware design for target application.
Keywords
SURF algorithm; Sub-IPs; DMA; Logic size; Object tracking;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 H.Bay, T.Tuvtelaars, and L.Van Gool, "Surf : Speeded up robust features", Computer Vision - ECCVI, Vol. 3951, pp. 404-417,, 2006
2 http://www.provartec.com.ipproducts/57, "PR201ConfigurableDual-coreHighPerformanceAH BDMAReferenceGuide",Revision1.5
3 Cordelia Schmid, Roger Mohr, Christian Bauckhage, "Evaluation of Interest Point Detectors", International Journal of Computer Vision, Vol. 37, Issue 2, pp. 151-172, June 2000.   DOI
4 Krystian Mikolajczyk, Cordelia Schmid, "A Performance Evaluation of Local Descriptors", IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, Issue. 10, pp. 1615-1630, October 2005.   DOI   ScienceOn
5 Takashi Saegusa, Tsutomu Maruyama and Yoshiki Yamaguchi, "HoW fast in an FPGA in image processing?", International Conference on Filed Programmable Logic and Applications, pp. 77-82, September 2008
6 Belt, H.J.W., "Word length reduction for the integral image", 15th IEEE international conference on Image Processing, pp. 805-808, 2008
7 Bouris, D, Nikitakis, A and Papaefstathiou, I. "Fast and Efficient FPGA-based Feature Detection employing the SURF algorithm". 18th IEEE Annual International Symposium on Field-Programmable Custom Computing Machines, pp. 3-10, 2010.
8 Ehsan, S., McDonald-Maier, K.D., "Exploring Integral Image Word Length Reduction Techniques for SURF Detector", '09 Second International Conference on Computer and Electrical Engineering, Vol. 1, pp. 635-639, 2009.
9 Jan Svab, Tomas Krajnnik, Jan Faigl and Libor Preucil. "FPGA Based Speeded Up Robust Features". In IEEE International Conference on Technologies for Practical Robot Applications, pp. 35-41, 2009.
10 Jae-Kyung Ryu, Su-Hyun Lee, Yong-Jin Jeong, "FPGA Design of a SURF-based Feature Extractor", Journal of Korea Multimedia Society, Vo.14, No.3, pp. 368-377, March 2011.   DOI   ScienceOn
11 Marius Muja, David G..Lowe, "Fast Approximate Nearest neighbors with automatic algorithm configuration", International conference on Computer Vision Theory and Applications, pp. 331-340, 2009.
12 Personal communication with industry, 2012.
13 Alper Yilmaz, Omar Javed, Mubarak Shah, "Object tracking : A Survey", ACM Computing Surveys, Vol.38, No.4, Article 13, December 2006.