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research-article

Fast and Efficient Pedestrian Detection via the Cascade Implementation of an Additive Kernel Support Vector Machine

Authors:

Jeonghyun Baek,

Euntai KimAuthors Info & Claims

IEEE Transactions on Intelligent Transportation Systems, Volume 18, Issue 4

Pages 902 - 916

https://doi.org/10.1109/TITS.2016.2594816

Published: 01 April 2017 Publication History

Abstract

For reliable driving assistance or automated driving, pedestrian detection must be robust and performed in real time. In pedestrian detection, a linear support vector machine (linSVM) is popularly used as a classifier but exhibits degraded performance due to the multipostures of pedestrians. Kernel SVM (KSVM) could be a better choice for pedestrian detection, but it has a disadvantage in that it requires too much more computation than linSVM. In this paper, the cascade implementation of the additive KSVM (AKSVM) is proposed for the application of pedestrian detection. AKSVM avoids kernel expansion by using lookup tables, and it is implemented in cascade form, thereby speeding up pedestrian detection. The cascade implementation is trained by a genetic algorithm such that the computation time is minimized, whereas the detection accuracy is maximized. In experiments, the proposed method is tested with the INRIA dataset. The experimental results indicate that the proposed method has better detection accuracy and reduced computation time compared with conventional methods.

References

[1]

P. Dollar, C. Wojek, B. Schiele, and P. Perona, “Pedestrian detection: An evaluation of the state of the art,” IEEE Trans. Pattern Anal. Mach. Intell., vol. Volume 34, no. Issue 4, pp. 743–761, 2012.

Digital Library

[2]

Y. Xu, D. Xu, S. Lin, T. X. Han, X. Cao, and X. Li, “Detection of sudden pedestrian crossings for driving assistance systems,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. Volume 42, no. Issue 3, pp. 729–739, 2012.

Digital Library

[3]

T. Gandi and M. M. Trivedi, “Pedestrian protection systems: Issues, survey, and challenges,” IEEE Trans. Intell. Transp. Syst., vol. Volume 8, no. Issue 3, pp. 413–430, 2007.

Digital Library

[4]

S. Zhang, C. Bauckhage, and A. B. Cremers, “Efficient pedestrian detection via rectangular features based on a statistical shape model,” IEEE Trans. Intell. Transp. Syst., vol. Volume 16, no. Issue 2, pp. 763–775, 2015.

[5]

A. Prioletti, A. Mogelmose, P. Grisleri, M. M. Trivedi, A. Broggi, and T. B. Moeslund, “Part-based pedestrian detection and feature-based tracking for driver assistance: Real-time, robust algorithms, and evaluation,” IEEE Trans. Intell. Transp. Syst., vol. Volume 14, no. Issue 3, pp. 1346–1359, 2013.

Digital Library

[6]

W. Liu, B. Yu, C. Duan, L. Chai, H. Yuan, and H. Zhao, “A pedestrian-detection method based on heterogeneous features and ensemble of multi-view-pose parts,” IEEE Trans. Intell. Transp. Syst., vol. Volume 16, no. Issue 2, pp. 813–824, 2015.

[7]

M. Pedersoli, J. Gonzalez, X. Hu, and X. Roca, “Toward real-time pedestrian detection based on a deformable templete model,” IEEE Trans. Intell. Transp. Syst., vol. Volume 15, no. Issue 1, pp. 355–364, 2014.

Digital Library

[8]

Q. Ye, Z. Han, J. Jiao, and J. Liu, “Human detection in images via piecewise linear support vector machines,” IEEE Trans. Image Process., vol. Volume 22, no. Issue 2, pp. 778–789, 2013.

Digital Library

[9]

P. Viola, M. J. Jones, and D. Snow, “Detecting pedestrians using patterns of motion and appearance,” in Proc. Int. Conf. Comput. Vis., 2003, pp. 734–741.

Digital Library

[10]

Y. Mu, S. Yan, Y. Liu, T. Huang, and B. Zhou, “Discriminative local binary patterns for human detection in personal album,” in Proc. CVPR, 2008, pp. 1–8.

[11]

T. Serre, L. Wolf, S. Bileschi, M. Riesenhuber, and T. Poggio, “Robust object recognition with cortex-like mechanisms,” IEEE Trans. Pattern Anal. Mach. Intell., vol. Volume 29, no. Issue 3, pp. 411–426, 2007.

Digital Library

[12]

N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proc. CVPR, 2005, pp. 886–893.

Digital Library

[13]

P. Dollár, Z. Tu, P. Perona, and S. Belongie, “Integral channel features,” in Proc. BMVC, 2009, pp. 1–11.

[14]

X. Wang, T. X. Han, and S. Yan, “An HOG-LBP human detector with partial occlusion handling,” in Proc. ICCV, 2009, pp. 32–39.

[15]

P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan, “Object detection with discriminatively trained part-based models,” IEEE Trans. Pattern Anal. Mach. Intell., vol. Volume 32, no. Issue 9, pp. 1627–1645, 2010.

Digital Library

[16]

T. Watanabe, S. Ito, and K. Yokoi, “Co-occurrence histograms of oriented gradients for pedestrian detection,” in Proc. IEEE Pacific-Rim Symp. Image Video Technol., 2009, pp. 37–47.

Digital Library

[17]

Y. Pang, H. Yan, Y. Yuan, and K. Wang, “Robust coHOG feature extraction in human-centered image/video management system,” IEEE Trans. Syst., Man, Cybern. B, Cybern., vol. Volume 42, no. Issue 2, pp. 458–468, 2012.

Digital Library

[18]

M. Hiromoto and R. Miyamoto, “Cascade classifier using divided CoHOG features for rapid pedestrian detection,” in Proc. Int. Conf. Comput. Vis. Syst., 2009, pp. 53–62.

Digital Library

[19]

H. Ren, C.-K. Heng, W. Zheng, L. Liang, and X. Chen, “Fast object detection using boosted co-occurrence histograms of oriented gradients,” in Proc. ICIP, 2010, pp. 2705–2708.

[20]

J. Yu, R. Miyamoto, and T. Onoye, “A speed-up scheme based on multiple-instance pruning for pedestrian detection using a support vector machine,” IEEE Trans. Image Process., vol. Volume 22, no. Issue 12, pp. 4752–4761, 2013.

Digital Library

[21]

J. Kim, J. Baek, and E. Kim, “A novel on-road vehicle detection method using <inline-formula><tex-math notation=LaTeX>$\pi\text{HOG}$</tex-math></inline-formula>,” IEEE Trans. Intell. Transp. Syst., vol. Volume 16, no. Issue 6, pp. 3414–3429, 2015.

[22]

S. Maji, A. C. Berg, and J. Malik, “Efficient classification for additive kernel SVMs,” IEEE Trans. Pattern Anal. Mach. Intell., vol. Volume 35, no. Issue 1, pp. 66–77, 2013.

Digital Library

[23]

H. Zhang, A. C. Berg, M. Maire, and J. Malik, “SVM-KNN: Discriminative nearest neighbor classification for visual category recognition,” in Proc. CVPR, 2005, pp. 2126–2136.

Digital Library

[24]

H. Cheng, P.-N. Tan, and R. Jin, “Efficient algorithm for localized support vector machine,” IEEE Trans. Knowl. Data Eng., vol. Volume 22, no. Issue 4, pp. 537–549, 2010.

Digital Library

[25]

S. Boughorbel, J.-P. Tarel, and N. Boujemaa, “Generalized histogram intersection kernel for image recognition,” in Proc. ICIP, 2005, pp. 161–164.

[26]

S. Maji, U. C. Berkeley, and A. C. Berg, “Classification using intersection kernel support vector machines is efficient,” in Proc. CVPR, 2008, pp. 1–8.

[27]

M. Varma and D. Ray, “Learning the discriminative power-invariance trade-off,” in Proc. ICCV, 2007, pp. 1–8.

[28]

C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn., vol. Volume 20, no. Issue 3, pp. 273–297, 1995.

[29]

L. Davis, Handbook of Genetic Algorithms, vol. Volume 115 . New York, NY, USA: Van Nostrand Reinhold, 1991.

[30]

J. Baek, H. Lee, B. Lee, H. Lee, and E. Kim, “An efficient genetic selection of the presentation order in simplified fuzzy ARTMAP patterns,” Appl. Soft Comput., vol. Volume 22, pp. 101–107, 2014.

Digital Library

[31]

R. Palaniappan and C. Eswaran, “Using genetic algorithm to select the presentation order of training patterns that improves simplified fuzzy ARTMAP classification performance,” Appl. Soft Comput., vol. Volume 9, no. Issue 1, pp. 100–106, 2009.

Digital Library

[32]

I. M. Oliver, Dj. Smith, and J. R. C. Holland, “A study of permutation crossover operators on the traveling salesman problem,” Texas Instrum. Ltd., Dallas, TX, USA, 1984.

[33]

D. E. Goldberg and R. Lingle, “Alleles, loci, and the traveling salesman problem,” in Proc. 1st Int. Conf Genetic Algorithms, 1985, pp. 154–159.

Digital Library

[34]

H. Lee and E. Kim, “A symbiotic evolutionary design of error-correcting code with minimal power consumption,” Electron. Telecommun. Res. Instit. J., vol. Volume 30, no. Issue 6, pp. 799–806, 2008.

[35]

P. Viola and M. Jones, “Robust real-time face detection,” Int. J. Comput. Vis., vol. Volume 57, no. Issue 2, pp. 137–154, 2004.

Digital Library

[36]

S. Walk, N. Majer, K. Schindler, and B. Schiele, “New features and insights for pedestrian detection,” in Proc. CVPR, 2010, pp. 1030–1037.

[37]

P. Felzenszwalb, D. McAllester, and D. Ramanan, “A discriminatively trained, multiscale, deformable part model,” in Proc. CVPR, 2008, pp. 1–8.

[38]

W. Ouyang, X. Zeng, and X. Wang, “Modeling mutual visibility relationship with a deep model in pedestrian detection,” in Proc. CVPR, 2013, pp. 3222–3229.

Digital Library

[39]

P. Dollar, R. Appel, S. Belongie, and P. Perona, “Fast feature pyramids for object detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. Volume 36, no. Issue 8, pp. 1532–1545, 2014.

Digital Library

[40]

P. Dollar, S. Belongie, and P. Perona, “The fastest pedestrian detector in the west,” in Proc. BMVC, 2010, pp. 1–11.

[41]

C. Zhang and P. Viola, “Multiple-instance pruning for learning efficient cascade detectors,” in Proc. Adv. Neural Inform. Process. Syst., 2007, pp. 1–8.

Digital Library

[42]

P. Dollar, R. Appel, and W. Kienzle, “Crosstalk cascades for frame-rate pedestrian detection,” in Proc. ECCV, 2012, pp. 645–659.

[43]

A. Angelova, A. Krizhevsky, V. Vanhoucke, A. Ogale, and D. Ferguson, “Real-time pedestrian detection with deep network cascades,” in Proc. BMVC, 2015, pp. 1–12.

[44]

W. R. Schwartz, A. Kembhavi, D. Harwood, and L. S. Davis, “Human detection using partial least squares analysis,” in Proc. IEEE ICCV, 2009, pp. 24–31.

[45]

C. Wojek and B. Schiele, “A performance evaluation of single and multi-feature people detection,” in Proc. DAGM, 2008, pp. 82–91.

Digital Library

[46]

P. Sabzmeydani and G. Mori, “Detecting pedestrians by learning shapelet,” in Proc. CVPR, 2007, pp. 1–8.

Cited By

Li YHuang NLiu KChen HWang ZYu J(2021)A Modified HOG Algorithm based on the Prewitt OperatorProceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing10.1145/3448748.3448789(257-261)Online publication date: 22-Jan-2021
https://dl.acm.org/doi/10.1145/3448748.3448789
Han BWang YYang ZGao X(2020)Small-Scale Pedestrian Detection Based on Deep Neural NetworkIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2019.292375221:7(3046-3055)Online publication date: 26-Jun-2020
https://dl.acm.org/doi/10.1109/TITS.2019.2923752
Min WFan MLi JHan Q(2019)Real‐time face recognition based on pre‐identification and multi‐scale classificationIET Computer Vision10.1049/iet-cvi.2018.558613:2(165-171)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1049/iet-cvi.2018.5586
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cover image IEEE Transactions on Intelligent Transportation Systems

IEEE Transactions on Intelligent Transportation Systems Volume 18, Issue 4

April 2017

300 pages

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IEEE Press

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Published: 01 April 2017

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Cited By

Li YHuang NLiu KChen HWang ZYu J(2021)A Modified HOG Algorithm based on the Prewitt OperatorProceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing10.1145/3448748.3448789(257-261)Online publication date: 22-Jan-2021
https://dl.acm.org/doi/10.1145/3448748.3448789
Han BWang YYang ZGao X(2020)Small-Scale Pedestrian Detection Based on Deep Neural NetworkIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2019.292375221:7(3046-3055)Online publication date: 26-Jun-2020
https://dl.acm.org/doi/10.1109/TITS.2019.2923752
Min WFan MLi JHan Q(2019)Real‐time face recognition based on pre‐identification and multi‐scale classificationIET Computer Vision10.1049/iet-cvi.2018.558613:2(165-171)Online publication date: 1-Mar-2019
https://dl.acm.org/doi/10.1049/iet-cvi.2018.5586
Bilal MHanif M(2019)High Performance Real-Time Pedestrian Detection Using Light Weight Features and Fast Cascaded Kernel SVM ClassificationJournal of Signal Processing Systems10.1007/s11265-018-1374-791:2(117-129)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1007/s11265-018-1374-7

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