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A Real-time Pedestrian Detection based on AGMM and HOG for Embedded Surveillance
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 Title & Authors
A Real-time Pedestrian Detection based on AGMM and HOG for Embedded Surveillance
Nguyen, Thanh Binh; Nguyen, Van Tuan; Chung, Sun-Tae;
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 Abstract
Pedestrian detection (PD) is an essential task in various applications and sliding window-based methods utilizing HOG (Histogram of Oriented Gradients) or HOG-like descriptors have been shown to be very effective for accurate PD. However, due to exhaustive search across images, PD methods based on sliding window usually require heavy computational time. In this paper, we propose a real-time PD method for embedded visual surveillance with fixed backgrounds. The proposed PD method employs HOG descriptors as many PD methods does, but utilizes selective search so that it can save processing time significantly. The proposed selective search is guided by restricting searching to candidate regions extracted from Adaptive Gaussian Mixture Model (AGMM)-based background subtraction technique. Moreover, approximate computation of HOG descriptor and implementation in fixed-point arithmetic mode contributes to reduction of processing time further. Possible accuracy degradation due to approximate computation is compensated by applying an appropriate one among three offline trained SVM classifiers according to sizes of candidate regions. The experimental results show that the proposed PD method significantly improves processing speed without noticeable accuracy degradation compared to the original HOG-based PD and HOG with cascade SVM so that it is a suitable real-time PD implementation for embedded surveillance systems.
 Keywords
Pedestrian Detection;Embedded Surveillance;HOG;AGMM;
 Language
English
 Cited by
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Real-time Human Detection under Omni-dir ectional Camera based on CNN with Unified Detection and AGMM for Visual Surveillance, Journal of Korea Multimedia Society, 2016, 19, 8, 1345  crossref(new windwow)
2.
A Framework for Human Body Parts Detection in RGB-D Image, Journal of Korea Multimedia Society, 2016, 19, 12, 1927  crossref(new windwow)
 References
1.
A. Shashua, Y. Gdalyahu, and G. Hayun, "Pedestrian Detection for Driving Assistance Systems: Single-Frame Classification and System Level Performance," Proceedings of IEEE Intelligent Vehicles Symposium, pp. 13-18, 2004.

2.
X. Wang, M. Wang, and W. Li, “Scene-Specific Pedestrian Detection for Static Video Surveillance,” IEEE Transactions on Software Engineering, Vol. 36, No. 2, pp. 361-374, 2014.

3.
J. Yan, X. Zhang, Z. Lei, S. Liao, and S.Z. Li, "Robust Multi-Resolution Pedestrian Detection in Traffic Scenes," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3033-3040, 2013.

4.
P. Viola and M. Jones. "Robust Real-Time Face Detection," International Journal of Computer Vision, Vol. 57, Issue 2, pp. 137-154, 2004. crossref(new window)

5.
N. Dalal and B. Triggs, "Histograms of Oriented Gradients for Human Detection," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 886-893, 2005.

6.
Q. Zhu, M.C. Yeh, and K.T. Cheng, "Fast Human Detection using a Cascade of Histograms of Oriented Gradients," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1491-1498, 2006.

7.
C.H. Lampert, M.B. Blaschko, and T. Hofmann. "Beyond Sliding Windows: Object Localization by Efficient Subwindow Search," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, 2008.

8.
C. Wojek and B. Schiele, "A Performance Evaluation of Single and Multi-Feature People Detection," Lecture Notes in Computer Science, Vol. 5096, pp. 82-91, 2008.

9.
O. Tuzel, F. Porikli, and P. Meer, “Pedestrian Detection via Classification on Riemannian Manifolds,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 30, No. 10, pp. 1713-1727, 2008. crossref(new window)

10.
X. Wang, T.X. Han, and S. Yan, "An HOGLBP Human Detector with Partial Occlusion Handling," Proceeding of IEEE International Conference on Computer Vision, pp. 32-39, 2009.

11.
P. Dollar, Z. Tu, P. Perona, and S. Belongie, "Integral Channel Features," Proceeindg of The British Machine Vision Conference, pp. 91.1-91.11, 2009.

12.
P. Dollar, C. Wojek, B. Schiele, and P. Perona, "Pedestrian Detection: A Benchmark," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 304-311, 2009.

13.
P. Dollar, S. Belongie, and P. Perona, "The Fastest Pedestrian Detector in the West," Proceeding of The British Machine Vision Conference, pp. 68.1- 68.11, 2010.

14.
P.F. Felzenszwalb, R. Girshick, D. McAllester, and D. Ramanan, “Object Detection with Discriminatively Trained Part-Based Models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, Issue 9, pp. 1627-1645, 2010. crossref(new window)

15.
D. Park, D. Ramanan, and C. Fowlkes, "Multiresolution Models for Object Detection," Proceeding of European Conference on Computer Vision, pp. 241-254, 2010.

16.
P. Dollar, C. Wojek, B. Schiele, and P. Perona, “Pedestrian Detection: An Evaluation of the State of the Art,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34, Issue 4, pp. 743-761, 2011. crossref(new window)

17.
M. Pedersoli, A. Vedaldi, and J. Gonzalez. "A Coarse-to-Fine Approach for Fast Deformable Object Detection," Proceeding of IEEE Conference Computer Vision and Pattern Recognition, pp. 1353-1360, 2011.

18.
P. Sudowe and B. Leibe, "Efficient Use of Geometric Constraints for Sliding-Window Object Detection in Video," Proceeding of International Conference on Computer Vision Systems, pp. 11-20, 2011.

19.
R. Benenson, M. Mathias, R. Timofte, and L. Van Gool, "Pedestrian Detection at 100 Frames per Second," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2903-2910, 2012.

20.
P. Sermanet, K. Kavukcuoglu, S. Chintala, and Y. LeCun, "Pedestrian Detection with Unsupervised Multi-Stage Feature Learning," Proceeding of IEEE Conference Computer Vision and Pattern Recognition, pp. 3626-3633, 2013.

21.
R. Benenson, M. Omran, J. Hosang, and B. Schiele, "Ten Years of Pedestrian Detection, What Have We Learned?," Proceeding of European Conference on Computer Vision, pp. 613-627, 2014.

22.
B. Hariharan, C.L. Zitnick, and P. Dollár, "Detecting Objects using Deformation Dictionaries," Proceeding of IEEE Conference Computer Vision and Pattern Recognition, pp. 1995-2002, 2014.

23.
K.M Bhuvanarjun and T.C. Mahalingesh, "Pedestrian Detection in a Video Sequence using HOG and Covaraince Method," International Journal of Electrical and Electronics Engineers, Vol. 7, Issue 1, pp. 183-190, 2015.

24.
K. van de Sande, J. Uijlings, T. Gevers, and A. Smeulders, "Segmentation as Selective Search for Object Recognition," Proceeding of IEEE International Conference on Computer Vision, pp. 1879-1886, 2011.

25.
J. Hosang, R. Benenson, Piotr Dollár, and B. Schiele,“ What Makes for Effective Detection Proposals?,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Accepted, 2015.

26.
OpenCV Library, http://opencv.org/, 2015. 11. 30.

27.
X. Yang, C. Yi, L. Cao, and Y. Tian, "Media-CCNY at TRECVID 2012: Surveillance Event Detection," NIST Trecvid Workshop, 2012.

28.
C. Stauffer and C, W.E.L Grimson, "Adaptive Background Mixture Models for Real-Time Tracking," Proceeding of Conference on Computer Vision and Pattern Recognition, pp. 246-252, 1999.

29.
T.B. Nguyen, S.T. Chung, and S.W. Cho, “An Effective Moving Cast Shadow Removal in Gray Level Video for Intelligent Visual Surveillance,” Journal of Korea Multimedia Society, Vol. 17, No. 4, pp. 420-432, 2014. crossref(new window)

30.
INRIA Person Dataset, http://pascal.inrialpes.fr/data/human/, 2015. 11. 30.