Optic flow estimation by support vector regression

被引：10

作者：

Colliez, Johan ^{[1
]}

Dufrenois, Franck ^{[1
]}

Hamad, Denis ^{[1
]}

机构：

[1] Univ Littoral Cote dOpale, Lab Anal Syst Littoral, F-62228 Calais, France

来源：

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2006年 / 19卷 / 07期

关键词：

optic flow; motion estimation; support vector regression;

D O I：

10.1016/j.engappai.2006.05.006

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, we describe an approach to estimate optic flow from an image sequence based on Support Vector Regression (SVR) machines with an adaptive E-margin. This approach uses affine and constant models for velocity vectors. Synthetic and real image sequences are used in order to compare results of the SVR approach against other well-known optic flow estimation methods. Experimental results on real traffic sequences show that SVR approach is an appropriate solution for object tracking. (c) 2006 Elsevier Ltd. All rights reserved.

引用

页码：761 / 768

页数：8

共 14 条

[1]

Aksoy S, 2000, INT C PATT RECOG, P870, DOI 10.1109/ICPR.2000.903054

[2]

[Anonymous], 1997, Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing

[3] Robust optic flow computation [J].

Bab-Hadiashar, A ;

Suter, D .

INTERNATIONAL JOURNAL OF COMPUTER VISION, 1998, 29 (01) :59-77

[4]

BARNUM P, 2003, EXPLORING PRACTICAL

[5] PERFORMANCE OF OPTICAL-FLOW TECHNIQUES [J].

BARRON, JL ;

FLEET, DJ ;

BEAUCHEMIN, SS .

INTERNATIONAL JOURNAL OF COMPUTER VISION, 1994, 12 (01) :43-77

[6]

Black M. J., 1993, [1993] Proceedings Fourth International Conference on Computer Vision, P231, DOI 10.1109/ICCV.1993.378214

[7] DETERMINING OPTICAL-FLOW [J].

HORN, BKP ;

SCHUNCK, BG .

ARTIFICIAL INTELLIGENCE, 1981, 17 (1-3) :185-203

[8]

Lucas B. D., 1981, IJCAI 81 7 INT JOINT, V2, P674

[9] AN INVESTIGATION OF SMOOTHNESS CONSTRAINTS FOR THE ESTIMATION OF DISPLACEMENT VECTOR-FIELDS FROM IMAGE SEQUENCES [J].

NAGEL, HH ;

ENKELMANN, W .

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1986, 8 (05) :565-593

[10]

SCHOLKOPF B, 2002, LEARNING KERNELS, V9, P251

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