FPGA-based circular hough transform with graph clustering for vision-based multi-robot tracking

Irwansyah A, Ibraheem OW, Hagemeyer J, Porrmann M, Rückert U (2015)
In: ReConFigurable Computing and FPGAs (ReConFig), 2015 International Conference on. IEEE: 1-8.

Conference Paper | Published | English

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Abstract
Shape-based object detection and recognition are frequently used methods in the field of computer vision. A well-known algorithm for circle detection is the Circular Hough Transform (CHT). This Hough Transform algorithm needs a huge memory space and large computational resources. Field Programmable Gate Array (FPGA)-based hardware accelerators can be used to efficiently handle such compute-intensive applications. In this paper, we present a resource-efficient FPGA-based architecture for the CHT algorithm. Additionally, we introduce a unique approach by combining the CHT algorithm with graph clustering. The combination of these algorithms and their implementation on a Xilinx Virtex-4 FPGA is used to support real-time vision-based multi-robot tracking. Furthermore, an efficient architecture is proposed to significantly reduce the required memory in the CHT module. For the Graph Clustering module, a multiplier-less distance calculation unit is implemented, significantly reducing the required FPGA resources. The proposed CHT design can handle multi-robot localization with an accuracy of 97 %, supporting a maximum video resolution of 1024x1024 with 128 frames per second, resulting in 134 MPixel/s. Our design provides significantly higher throughput compared to other implementations on embedded processors, FPGAs, and general purpose CPUs. Compared to an OpenCV implementation on a 3.2 GHz desktop CPU, our implementation achieves a speed-up of more than 5.7.
Publishing Year
Conference
International Conference on ReConFigurable Computing and FPGAs (ReConFig)
Location
Riviera Maya, Mexico
Conference Date
2015-12-07 – 2015-12-09
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Irwansyah A, Ibraheem OW, Hagemeyer J, Porrmann M, Rückert U. FPGA-based circular hough transform with graph clustering for vision-based multi-robot tracking. In: ReConFigurable Computing and FPGAs (ReConFig), 2015 International Conference on. IEEE; 2015: 1-8.
Irwansyah, A., Ibraheem, O. W., Hagemeyer, J., Porrmann, M., & Rückert, U. (2015). FPGA-based circular hough transform with graph clustering for vision-based multi-robot tracking. ReConFigurable Computing and FPGAs (ReConFig), 2015 International Conference on, 1-8.
Irwansyah, A., Ibraheem, O. W., Hagemeyer, J., Porrmann, M., and Rückert, U. (2015). “FPGA-based circular hough transform with graph clustering for vision-based multi-robot tracking” in ReConFigurable Computing and FPGAs (ReConFig), 2015 International Conference on (IEEE), 1-8.
Irwansyah, A., et al., 2015. FPGA-based circular hough transform with graph clustering for vision-based multi-robot tracking. In ReConFigurable Computing and FPGAs (ReConFig), 2015 International Conference on. IEEE, pp. 1-8.
A. Irwansyah, et al., “FPGA-based circular hough transform with graph clustering for vision-based multi-robot tracking”, ReConFigurable Computing and FPGAs (ReConFig), 2015 International Conference on, IEEE, 2015, pp.1-8.
Irwansyah, A., Ibraheem, O.W., Hagemeyer, J., Porrmann, M., Rückert, U.: FPGA-based circular hough transform with graph clustering for vision-based multi-robot tracking. ReConFigurable Computing and FPGAs (ReConFig), 2015 International Conference on. p. 1-8. IEEE (2015).
Irwansyah, Arif, Ibraheem, Omar Waleed, Hagemeyer, Jens, Porrmann, Mario, and Rückert, Ulrich. “FPGA-based circular hough transform with graph clustering for vision-based multi-robot tracking”. ReConFigurable Computing and FPGAs (ReConFig), 2015 International Conference on. IEEE, 2015. 1-8.
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