Covariance Tracking Algorithm on Bilateral Filtering under Lie Group Structure

Xie, Ying Hong; Wu, Cheng Dong

doi:10.4028/www.scientific.net/AMM.519-520.684

Paper Titles

Research on Geometric Positioning Algorithm of Vision System in High Speed and High Precision Chip Mounter
p.667

A Weighted Motion Compensation Interpolation Method for Improving Side Information in Distributed Video Coding
p.672

A New Image Irradiance Equation for Perspective Shape from Shading of Hybrid Surfaces
p.676

A Ship Detection Model in Optical Satellite Image
p.680

Covariance Tracking Algorithm on Bilateral Filtering under Lie Group Structure
p.684

Evaluation and Comparison of Thresholding Segmentation Techniques
p.689

Head Pose Estimation via Direction-Sensitive Feature and Random Regression Forests
p.693

Triangle Rasterizer Based on the Barycentric Arithmetic
p.697

Individual Tree Detection from High Spatial Resolution Imagery Using Color and Texture Features
p.703

HomeApplied Mechanics and MaterialsComputer and Information TechnologyCovariance Tracking Algorithm on Bilateral...

Covariance Tracking Algorithm on Bilateral Filtering under Lie Group Structure

Abstract:

The existing object tracking method using covariance modeling is hard to reach the desired tracking performance when the deformation of moving target and illumination changes are drastic, we proposed a object tracking algorithm based on bilateral filtering. Firstly, the algorithm deals the image to be tracked with bilateral filtering, and extracts the needed features of filtered image to construct covariance matrix as tracking model. Secondly, under log-Euclidean Riemannian metric, we construct similarity measure for object covariance matrix and model updating strategy. Extensive experiments show that the proposed method has better adaptability for object deformation and illumination changes.

Info:

Periodical:

Applied Mechanics and Materials (Volumes 519-520)

Main Theme:

Computer and Information Technology

Edited by:

Prasad Yarlagadda, Seung-Bok Choi and Yun-Hae Kim

Pages:

684-688

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.519-520.684

Citation:

Y. H. Xie and C. D. Wu, "Covariance Tracking Algorithm on Bilateral Filtering under Lie Group Structure", Applied Mechanics and Materials, Vols. 519-520, pp. 684-688, 2014

Online since:

February 2014

Authors:

Ying Hong Xie *, Cheng Dong Wu

Keywords:

Bilateral Filtering, Log-Euclidean, Object Tracking

Export:

RIS, BibTeX

Price:

$38.00

Permissions:

Request Permissions

* - Corresponding Author

Add to Cart

References

[1] LI G W, LIU Y P, YIN J , Target tracking with feature covariance based on an improved Lie group structure,. Chinese Journal of Scientific Instrument, vol. 31, no. 1, pp.111-116, (2010).

[2] WU Y, WANG J Q, Real-time visual tracking via incremental covariance model update on Log-Euclidean Riemannian manifold, Chinese Conference on Pattern Recognition, pp.1-5, (2009).

DOI: https://doi.org/10.1109/ccpr.2009.5344069

[3] GU Q Q, ZHOU J, A similarity measure under Log-euclidean metric for stereo matching, 19th International Conference on Pattern Recognition, pp.1-4, (2008).

DOI: https://doi.org/10.1109/icpr.2008.4761347

[4] DONOSER M, KLUCKNER S, Object tracking by structure tensor analysis, 20th International Conference on Pattern Recognition, pp.2600-2603, (2010).

DOI: https://doi.org/10.1109/icpr.2010.637

[5] WEN J, GAO XB. Incremental Learning of weighted tensor subspace for visual tracking, Proceedings of the 2009 IEEE International Conference on Systems, Man, and Cybernetics, pp.3688-3693, (2009).

DOI: https://doi.org/10.1109/icsmc.2009.5346874

[6] LI L, YAN H, Cost aggregation strategy with bilateral filter based on multi-scale nonlinear structure tensor, Journal of Networks, vol, 6, no. 7, pp.958-965, (2011).

DOI: https://doi.org/10.4304/jnw.6.7.958-965

[7] WANG S H, YOU H J, BFSIFT: A novel method to find feature matches for SAR image registration, IEEE Geoscience and Remote Sensing Letters, vol. 9, no. 4, pp.649-653, (2012).

DOI: https://doi.org/10.1109/lgrs.2011.2177437

[8] LI X, HU W M, Visual tracking via incremental Log-Euclidean Riemannian subspace learning, IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, (2008).

DOI: https://doi.org/10.1109/cvpr.2008.4587516

Paper TitlePages

A Data Stream Outlier Detection Algorithm Based on Reverse K Nearest Neighbors

Authors: Zhong Ping Zhang, Yong Xin Liang

Abstract: This paper proposes a new data stream outlier detection algorithm SODRNN based on reverse nearest neighbors. We deal with the sliding window model, where outlier queries are performed in order to detect anomalies in the current window. The update of insertion or deletion only needs one scan of the current window, which improves efficiency. The capability of queries at arbitrary time on the whole current window is achieved by Query Manager Procedure, which can capture the phenomenon of concept drift of data stream in time. Results of experiments conducted on both synthetic and real data sets show that SODRNN algorithm is both effective and efficient.

1032

Study of Missile Radiator Predicting and Tracking Technology on the Basis of Kalman Filter and Mean-Shift Algorithm

Authors: Zhen Hui Xu, Bao Quan Mao, Li Xu, Jun Yan Zhao

Abstract: In order to improve the real-time character of missile radiator tracking and solve the predicting tracking problem when missile radiator shortly shelter or missing, it introduces moving target predicting and tracking technology. According to the predicting and tracking method, it proposes three predicting and tracking overall schemes of missile radiator based on Kalman filtering and improved Mean-Shift algorithm. Also it compares the real-time character of three kinds of schemes. According to the trajectory character of missile radiator, it constructs Kalman filter. The experiment results indicate that by using Kalman filtering technology, there are improvements in real-time character and shortly shelter or missing problem can be solved well. It plays a certain compensation function to the whole system.

1584

Fingerprint Matching Based on Orientation Feature

Authors: Ravinder Kumar, Pravin Chandra, M. Hanmandlu

Abstract: This paper presents a fast and reliable algorithm for fingerprint verification. Our proposed fingerprint verification algorithm is based on image-based fingerprint matching. The improved orientation feature vector of two fingerprints has been compared to compute the similarities at a given threshold. Fingerprint image has been aligned by rotating through an angle before feature vector is computed and matched. Row and Column variance feature vector of orientation image will be employed. The algorithm has been tested on the FVC2002 Databases. The performance of algorithm is measured in terms of GAR and FAR. At a threshold level of 1.1 % and at 5.7% FAR the GAR observed is 97.83%. The improved Feature vector will lower imposter acceptance rate at reasonable GAR and hence yields better GAR at lower FAR. The proposed algorithm is computationally very efficient and can be implemented on Real-Time Systems.

888

A Method of Detecting Circle by Improved Hough Transform

Authors: Xue Feng Wu, Yu Fan

Abstract: A new algorithms for parameters of an image irregular boundary circle parameters is presented, which is based on “Curve-Approximate Method” .For a set of an image circle boundary points by image pre-processing, firstly this paper introduces a substitute variant curve approximate reputably while picking out the irregular boundary points in all points, until to fit the terminate condition. Finally, it succeeds to get the optimal estimation of parameters of a circle. Example show that the algorithms runs more quickly and automatically than traditional generalized hough transform, and a good result is obtained if the irregular boundary points is small proportion in all points of a circle.

639

A Novel High Accuracy Sub-Pixel Corner Detection Algorithm for Camera Calibration

Authors: Fang Jie Yu, Xin Luan, Da Lei Song, Xiu Fang Li, Hong Hong Zhou

Abstract: This paper presents a novel sub-pixel corner detection algorithm for camera calibration. In order to achieve high accuracy and robust performance, the pixel level candidate regions are firstly identified by Harris detector. Within these regions, the center of gravity (COG) method is used to gain sub-pixel corner detection. Instead of using the intensity value of the regions, we propose to use corner response function (CRF) as the distribution of the weights of COG. The results of camera calibration experiments show that the proposed algorithm is more accurate and robust than traditional COG sub-pixel corner detection methods.

713