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2016 | Vol. 21, no. 3 | 27--44
Tytuł artykułu

FPGA Implementation of Multi-scale Face Detection Using HOG Features and SVM Classifier

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
In this paper an FPGA based embedded vision system for face detection is presented. The sliding detection window, HOG+SVM algorithm and multi-scale image processing were used and extensively described. The applied computation parallelizations allowed to obtain real-time processing of a 1280 × 720 @ 50Hz video stream. The presented module has been verified on the Zybo development board with Zynq SoC device from Xilinx. It can be used in a vast number of vision systems, including diver fatigue monitoring.
Słowa kluczowe
EN
face detection   HOG   SVM   FPGA  
Wydawca

Rocznik
Strony
27--44
Opis fizyczny
Bibliogr. 25 poz., rys., tab.
Twórcy
autor
  • AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków
autor
  • AGH University of Science and Technology, al. Mickiewicza 30, 30-059 Kraków
Bibliografia
  • [1] Bailey, D.G. (2011). Design for Embedded Image Processing on FPGAs. Wiley
  • [2] Bryla, M. (2015). Smart camera for people recognition based on face image implemented on the heterogeneous Zynq platform. Master Thesis, AGH University of Science and Technology
  • [3] Dalal, N., Triggs, B. (2005). Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), 1, 886–893
  • [4] Das, S., Jariwala, A., Engineer, P. (2012). Modified architecture for real-time face detection using FPGA. In Engineering (NUiCONE), 2012 Nirma University International Conference on, pp. 1–5
  • [5] Gao, C., Lu, S-L. (2008). Novel FPGA based Haar classifier face detection algorithm acceleration. In Field Programmable Logic and Applications, 2008. FPL 2008. International Conference on, pp. 373–378
  • [6] Hu, K.T., Pai, Y.T., Ruan, S.J., Naroska, E. (2010). A hardware-efficient color segmentation algorithm for face detection. In Circuits and Systems (APCCAS), 2010 IEEE Asia Pacific Conference on, pp. 688–691
  • [7] Huang, C., Vahid, F. (2011). Scalable object detection accelerators on FPGAs using custom design space exploration. In Application Specific Processors (SASP), 2011 IEEE 9th Symposium on, pp. 115–121
  • [8] Komorkiewicz, M., Kluczewski, M., Gorgon, M. (2012). Floating point HOG implementation for real-time multiple object detection. In 22nd International Conference on Field Programmable Logic and Applications (FPL), pp. 711–714
  • [9] Kotarba, D. (2016). Embedded vision system for human silhouette detection using HOG and SVM approach. Bachelor Thesis, AGH University of Science and Technology
  • [10] Kryjak, T., Gorgon, M., Komorkiewicz, M. (2016). An Efficient Hardware Architecture for Block Based Image Processing Algorithms, pages 54–65. Springer International Publishing, Cham
  • [11] Lai, H.C., Savvides, M., Chen, T. (2007). Proposed fpga hardware architecture for high frame rate face detection using feature cascade classifiers. In Biometrics: Theory, Applications, and Systems, 2007. BTAS 2007. First IEEE International Conference on, pp. 1–6
  • [12] Lee, Y., Ko, S.-B. (2006). FPGA implementation of a face detector using neural networks. In Electrical and Computer Engineering, 2006. CCECE ’06. Canadian Conference on, pp. 1914–1917
  • [13] Li, H., Lin, Z., Shen, X., Brandt, J., Hua, G. (2015). A convolutional neural network cascade for face detection. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5325–5334
  • [14] Mustafah, Y.M., Azman, A.W. (2012). Skin region detector for real time face detection system. In Computer and Communication Engineering (ICCCE), 2012 International Conference on, pp. 653–658
  • [15] Nguyen, D.T., Ogunbona, P., Li, W. (2011). Human detection with contour-based local motion binary patterns. In Image Processing (ICIP), 2011 18th IEEE International Conference on, pp. 3609–3612
  • [16] Ojala, T., Pietikainen, M., Maenpaa, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7), 971–987
  • [17] Ojala, T., Pietikainen, M., Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 29(1), 51–59
  • [18] Suleiman, A., Sze, V. (2015). An energy-efficient hardware implementation of HOG-based object detection at 1080HD 60 fps with multi-scale support. Journal of Signal Processing Systems, pages 1–13
  • [19] Vapnik, V.N. (1995). The nature of statistical learning theory
  • [20] Viola, P., Jones, M. (2001). Robust real-time face detection. In Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, 2, 747–747
  • [21] Wang, N.J., Chang, S.C., Chou, P.J. (2012). A real-time multi-face detection system implemented on FPGA. In Intelligent Signal Processing and Communications Systems (ISPACS), 2012 International Symposium on, pp. 333–337
  • [22] Xilinx. (2011). LogiCORE IP Cordic v4.0Z, www.xilinx.com
  • [23] Yang, H.C., Wang, X.A. (2015). Cascade face detection based on histograms of oriented gradients and support vector machine. In 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), pages 766–770
  • [24] Zafeiriou, S., Zhang, C., Zhang, Z. (2015). A survey on face detection in the wild: Past, present and future. Computer Vision and Image Understanding, 138:1–24
  • [25] Zhang, L., Chu, R., Xiang, S., Liao, S., Li, S.Z. (2007). Face Detection Based on Multi-Block LBP Representation, pages 11–18. Springer Berlin Heidelberg, Berlin, Heidelberg
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
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Identyfikator YADDA
bwmeta1.element.baztech-00b5043f-b458-49f8-a3a8-7b287c810add
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