[논문]New Approach to Two-wheeler Detection using Correlation Coefficient based on Histogram of Oriented Gradients

Lee, Yeunghak; Shim, Jaechang

doi:10.9717/jmis.2016.3.4.119

New Approach to Two-wheeler Detection using Correlation Coefficient based on Histogram of Oriented Gradients 원문보기

The journal of multimedia information system, v.3 no.4, 2016년, pp.119 - 128

Lee, Yeunghak (Department of Avionics Engineering College of Aviation, Kyungwoon University) , Shim, Jaechang (Department of Computer Engineering, College of Engineering, Andong National University)

Abstract ▼ AI-Helper

This study aims to suggest a new algorithm for detecting two-wheelers on road that have various shapes according to the viewing angle for vision based intelligent vehicles. This article describes a new approach to two-wheelers detection algorithm riding on people based on modified Histogram of Oriented Gradients (HOG) using correlation coefficient (CC). The CC between two local area variables, in which one is the person riding a bike and other is its background, can represent correlation relation. First, we extract edge vectors using HOG which includes gradient information and differential magnitude as cell based. And then, the value, which is calculated by the CC between the area of each cell and one of two-wheelers, can be extracted as the weighting factor in process for normalizing the modified HOG cell. This paper applied the Adaboost algorithm to make a strong classification from weak classification. In this experiment, we can get the result that the detection rate of the proposed method is higher than that of the traditional method.

주제어

AI 본문요약
AI-Helper

* AI 자동 식별 결과로 적합하지 않은 문장이 있을 수 있으니, 이용에 유의하시기 바랍니다.

문제 정의

From the experimental results, we proved that the process of two wheelers detection may use smaller local features, low dimension and less computation than earlier suggested. And we consider that there are many future experiments that could be done to extend of this study (not deals with including occluded area, object change according to the weather, and others degree in this paper.
But it is one of the most difficult works due to a variety range of poses, as well as environmental conditions, cluttered backgrounds, and composite object (shown several shapes than pedestrian, according to the view point). So in this paper, we suggest a new algorithm to detect the two wheelers for the weak part on the road.
But while a simple device or vehicle to move things may be in past, the shape (or style) and function is very fast and variety progressed in now. The research for the vehicle was not only the improvement of the vehicle performance but also the protection of people in the vehicle after accident. Nowadays, the core of the research is changing to prevent the accident and to protect the human from inner and outer.

가설 설정

So it has very valuable research to protect the human life and to avoid the accident. To solve this problem, we proposed that HOG_CC2 used a sliding window approach has outstanding detection result than previous algorithm. Adaboost classification based methods have comprised the mainstream of research to detect two wheelers and have been shown to achieve successful results in two wheelers detection.

제안 방법

In section 2, this paper explains the original feature set using general HOG algorithm and the associated evolutionary algorithm which is improved the detection rate.
Because of above reasons, we tried to use modified HOG algorithm to select best features and Adaboost to improve detection rate. In this study, we invented new algorithm based on adapted HOG value which is normalized by correlation coefficient between two wheelers area and the cell. More detail about general and modified HOG will describe in section 2.
More detail about general and modified HOG will describe in section 2. This paper proposes a system for the detection of two wheelers ride on people with both efficiency and accuracy.
This paper tried two kinds of correlation coefficient (CC) methods: CC1 and CC2. The CC1 is calculated the CC value between each cell and special area which is showed human and two wheelers, as shown in Figure 7.

데이터처리

This representation yields not only a suggested solution for weak objects (two wheelers) on the road but computationally efficient algorithm using the correlation coefficient.

이론/모형

Our preliminary experiments were tested on our two wheelers data set, and Figure 6 shows the ROC curves for the traditional HOG method. The ROC curves were generated as described in [33] by adjusting the threshold (θ) of the last two node of the object detector from -20 to 20.
. Using two strong classifications, in this paper suggests 2^nd stage cascade method. It improves the recognition rate due to the complementary role for two feature vector of quite different type.

참고문헌 (38)

T. Gandhi and M. M. Trivedi, "Pedestrian Protection Systems: Issues, Survey," and Challenges. IEEE Transaction on Intelligent Transportation Systems, Vol. 8, No.3, pp. 413-430, 2007.

상세보기
S. Paisitkriangkrai, C. Shen, and J. Zhang, "Fast Pedestrian detection using a cascade of boosted covariance features," IEEE Transaction on Circuit and System for Video Technology, Vol. 18, No.8, pp.1140-1151, 2008.

상세보기
X.B. Cao, Y.W. Xu, D. Chen and H. Qiao, "Associated evolution of a support vector machine-based classifier for pedestrian detection," Information Sciences, vol. 179, no. 8, pp.1070-1077, 2009

상세보기
H. Cho, P. E. Rybski and W. Zhang, "Vision-based bicyclist detection and tracking for intelligent vehicles," 2010 IEEE Intelligent Vehicles Symposium, pp. 454-461, San Diego, CA., 2010.
M. Enzweiler and D.M. Gavrila, "Monocular Pedestrian Detection: Survey and Experiments," IEEE PAMI, vol. 31, no.12, pp. 2179-2195, 2008.
D. M. Gavrila, "Sensor-based pedestrian protection," IEEE Intelligent System, vol. 16, no. 6, pp. 77-81, 2001.

상세보기
Y. Xu, X. Cao, and H. Qiao, "An Efficient Tree Classifier Ensemble-based Approach for Pedestrian Detection," IEEE Transaction on System, Man and Cybernetics, vol. 41, no. 1, pp. 107-117, 2011.
X. Liu and K. Fujimura, "Pedestrian detection using stereo night vision," IEEE Veh. Technol. Vol. 53, No. 6, pp. 1657-1665, 2004.

상세보기
M. Bertozzi, A. Broggi, A. Lasagni and M. D. Rose, "Infrared stereo vision-based pedestrian detection," in Proc. IEEE Intell. Veh. Symp., pp. 24-29, 2005.
H. G. Jung, Y. H. Lee, P. J. Yoon, I. Y. Hwang and J. Kim, "Sensor fusion based obstacle detection/classification for active pedestrian protection system," in Proc. Adv. Vis. Comput., pp. 294-305, 2006.
I. P. Alonso, D. F. Llorca, M. A. Sotelo, L. M. Bergasa, P. R. de Toro, J. Nuevo, M. Ocana and A. G. Garrido, "Combination of feature extraction methods for SVM pedestrian detection. IEEE Trans. Pattern Anal. Mach. Intell., Vol. 8, No.2, 2007.
H. G. Jung, "Pedestrian Protection System: Review and Problems," KSAE 2010 Annual Conference, pp. 1820-1826, 2010.
O. Tuzel, F. Porikli and P. Meer, "Human detection via classification on Riemannian manifolds. in Proc. IEEE conf. comp. Vis. Pattern Recognition, pp. 1-8, 2007.
L. Yu, F. Zhao and Z. An, "Locally Assembled Binary Feature with Feed-forward Cascade for pedestrian Detection in Intelligent Vehicles," in Proc. 9th Int. Conf. on Cognitive Informatics, pp. 458-463, 2010.
L. Oliveria, P. Peixoto and U. Nunes, "A Hierarchical Fuzzy Integration of Local and Global Feature-based Classfiers to Recognize Objects in Autonomous Vehicles," Intl. conf. on Robotic Automation, pp. 45-50, 2007.
P. F. Felzenzwalb, R. B. Girshick, D. Mcallester and D. Ramanan, "Object Detection with Discriminatively Trained Part-Based Models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 32 no.9, pp. 1627-1645, 2010.

상세보기
K. Mikolajczyk, C. Schmid and A. Zisserman, "Human detection based on a probabilistic assembly of robust part detectors," in Proc. Eur.Conf. Comput. Vis., pp. 69- 81, 2004.
D. M. Gavrila and S. Munder, "Multi-cue pedestrian detection and tracking from a moving vehicle," Int. J. Comput. Vis. Vol. 73, No. 1, pp. 41-59, 2007.

상세보기
P. Sabzmeydani and G. Mori, "Detecting Pedestrians by learning shapelet features," in Proc. CVPR, pp. 1-8, 2007.
T. Ahonen, A. Hadid and M. Pietikainen, "Face description with local binary patters: Application to face recognition", IEEE Trans. Pattern Anal. Mach. Intell., vol. 28, no. 12, pp. 2037-2041, 2006.

상세보기
N. Dalal and B. Triggs, "Histogram of Oriented Gradients for Human Detection," IEEE Computer. Vision Pattern Recognition, pp.886-893, 2005.
C. S. Chen, C. Matsushima, Y. Yamauchi, T. Yamashita and H. Fujiyoshi, A Method for Reducing number of HOG Features based on Real Adaboost, IPSJ SIG Technical Repot, pp. 1-8, 2009.
S. Pavani, D. Delgado and A. F. Frangi, "Haar-like features with optionally weighted rectangles for rapid object detection," Pattern Recognition, Vol. 43, No. 1, pp.160-172, 2010.

상세보기
X. Y. Wang, T. X. Han and S. Yan, "An HOG-LBP human detector with partial occlusion handling," in Proc. ICCV, pp. 32-39, 2009.
S. H. Lee, Y. H. Lee, T. S. Kim and H, D. Seo, "Pedestrian Recognition using the Cascade Method of Independent Features based on Adaboost Algorithm," The Journal of KIIT, Vol. 8, No. 8, pp. 85-94, 2010.
D. Geronimo, A. M. Lopez, A. D. Sappa and T. Graf, "Survey of Pedestrian Detection for Advanced Driver Assistance Systems," IEEE Trans. Pattern Anal. Mach. Intell., Vol. 32, No. 7, pp. 1239-1258, 2010.

상세보기
S. Munder and D. M. Gavrila, "An Experimental Study on Pedestrian Classification," IEEE Trans. Pattern Anal. Mach. Intell., Vol. 28, No. 11, pp.1-6, 2006.

상세보기
Y. H. Lee, J. Y. Ko, J. H. Suk, T. M. Roh and J. C. Shim, "Pedestrian Recognition using Adaboost Algorithm based on Cascade Method by Curvature and HOG," The Journal of KIISE, Vol. 16, No. 6, pp. 652-662, 2010.
C. Papageorgiou and T. Poggio, "A trainable system for object detection," International Journal of Computer Vision, vol. 38, no. 1, pp.15-33, 2000.
R. Fransens, R. V. Depoortere and J. D. Prins, "Boundary based feature selection," Technical report, 2002.
D. M. Gavrila and V. Philomin, "Real-time object detection for "smart" vehicles," in Proc. of ICCV, Vol. 1, pp. 87-93, 1999.
P. Viloa, M. Jones and M. Snow, "Detecting pedestrians using patterns of motion and appearance. The 9th ICCV, pp. 153-161, 2003.
R. Ronfard, C. Schmid and B. Triggs, "Learning to parse pictures of people," The 7th ECCV, pp. 700-714, 2002.
F. Porikli, "Integral histogram: a fast way to extract histograms in Cartesian spaces," CVPR 2005, vol. 1, pp. 829-836, 2005
http://en.wikipedia.org/wiki/Receiver_operating_characteristic, 2016.
M. Oren, C. Papageorgiou, P. Sinha, E. Osuna and T. Poggio, "Pedestrian detection using wavelet templates," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 193-199, 1997.
R. Lienhart and J. Maydt, "An Extended Set of Haarlike Features for Rapid Object Detection," IEEE ICIP 2002, Vol. 1, pp. 900-903, 2002.
Y. Lee, T. Kim, S. Lee, and J. Shim, "Spatial Regions Periodicity based Detection of Two-wheelers using Histogram of Oriented Gradients," International Journal of Multimedia and Ubiquitous Engineering, Vol.10, No.4, pp.325-336, 2015.

상세보기

저자의 다른 논문 :

표제어: PCR

동의어: Packet Collision Rate

용어 설명 출처 목록 (6)

용어 설명: PCR은 세균 특이성이 있는 primer를 이용하여 적은 수의 세균이 있을지라도 쉽게 검출할 수 있는 유용한 방법이며, 이를 이용하여 구강 내 치면세균막이나 타액에서 직접 세균을 검출할 수 있게 되었다[8].

내보내기 구분	파일저장 인쇄 메일전송
구성항목	기본정보 상세정보 관리번호, 논문명, 저널/프로시딩명, 저자 , 발행년, 권, 호, 시작페이지, 끝페이지, 발행기관 관리번호, 논문명, 대등논문명, 저자 , 저널/프로시딩명, 발행기관, 발행년, 발행언어, 권, 호, 시작페이지, 끝페이지, ISBN, ISSN, 주제분야, 키워드, 초록(한글), 초록(영문), 저자(소속기관)
저장형식	Text(ASCII format) Excel format RefWorks Direct Export RIS format (for Reference Manager, ProCite, EndNote), Scholar's Aids, Mendeley
메일정보	받는사람 (필수) @ 보내는사람 (선택) @ 제목 내용 KISTI 검색결과 이메일 서비스
안내	총 건의 자료가 검색되었습니다. 다운받으실 자료의 인덱스를 입력하세요. (1-10,000) 검색결과의 순서대로 최대 10,000건 까지 다운로드가 가능합니다. 데이타가 많을 경우 속도가 느려질 수 있습니다.(최대 2~3분 소요) 다운로드 파일은 UTF-8 형태로 저장됩니다. 파일의 내용이 제대로 보이지 않을실 때는 웹브라우저 상단의 보기 -> 인코딩 -> 자동선택 여부를 확인하십시오. ~ Text(ASCII format) Excel format

연합인증