Robust object tracking in mobile robots using object features and on-line learning based particle filter

被引：1

作者：

Lee, Hyung-Ho ^{[1
]}

Cui, Xuenan ^{[2
]}

Kim, Hyoung-Rae ^{[2
]}

Ma, Seong-Wan ^{[2
]}

Lee, Jae-Hong ^{[2
]}

Kim, Hak-Il ^{[2
]}

机构：

[1] LIG Nex, Korea, Republic of

[2] Inha University, Korea, Republic of

来源：

Journal of Institute of Control, Robotics and Systems | 2012年 / 18卷 / 06期

关键词：

Color histogram; HOG; IPCA; Mobile robots; Object tracking; Particle filter;

D O I：

10.5302/J.ICROS.2012.18.6.562

中图分类号：

学科分类号：

摘要：

This paper proposes a robust object tracking algorithm using object features and on-line learning based particle filter for mobile robots. Mobile robots with a side-view camera have problems as camera jitter, illumination change, object shape variation and occlusion in variety environments. In order to overcome these problems, color histogram and HOG descriptor are fused for efficient representation of an object. Particle filter is used for robust object tracking with on-line learning method IPCA in non-linear environment. The validity of the proposed algorithm is revealed via experiments with DBs acquired in variety environment. The experiments show that the accuracy performance of particle filter using combined color and shape information associated with online learning (92.4 %) is more robust than that of particle filter using only color information (71.1 %) or particle filter using shape and color information without on-line learning (90.3 %). © ICROS 2012.

引用

页码：562 / 570

页数：8

共 22 条

[1]

Yilmaz A., Javed O., Shah M., Object tracking: a survey, ACM Computing Surveys, 38, 4, (2006)

[2]

Hieu T.N., Arnold W.M.S., Fast occluded object tracking by a robust appearance filter, IEEE Transaction on Pattern Analysis and Machine Intelligence, 26, 8, pp. 1099-1104, (2004)

[3]

Zuo W., Zhang D., Bidirectional PCA with assembled matrix distance metric for image recognition, IEEE Transactions on Systems, Man, and Cybernetics, 36, 4, pp. 863-872, (2006)

[4]

Xu F., Gao M., Human detection and tracking based on HOG and particle filter, International Congress on Image and Signal Processing, pp. 1503-1507, (2010)

[5]

Bradski G.R., Clara S., Computer vision face tracking for use in a perceptual user interface, Intel Journal, (1998)

[6]

Comaniciu D., Ramesh V., Mean shift and optimal prediction for efficient object tracking, International Conference on Image Processing, 3, pp. 70-73, (2000)

[7]

Allen J.G., Xu R.Y.D., Jin J.S., Object tracking using camshift algorithm and multiple quantized feature spaces, Proceedings of Pan-Sydney area workshop on Visual Information Processing, pp. 3-7, (2004)

[8]

Corrales J.A., Gil P., Candelas F.A., Torres F., Tracking based on hue-saturation features with a miniaturized active vision system, Proc. of the 40th International Symposium on Robotics, pp. 107-112, (2009)

[9]

Xuena Q., Qiang L., Target tracking and localization of binocular mobile robot using CAMShift and SIFT, Proc. of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation, pp. 483-487, (2009)

[10]

Kim J.H., Park C.H., Kweon I.-S., Visual tracking for non-rigid objects using rao-blackwellized particle filter, IEEE International Conference on Robotics and Automation, pp. 4537-4544, (2010)

← 1 2 3 →