Moving Target Tracking Using Sparse Optical Flow Method

Tian Ding Chen; Jian Hu; Chao Lu; Zhong Jiao He

doi:10.4028/www.scientific.net/AMR.718-720.2335

Paper Titles

Fast Loop Boundary Coverage Testing in Symbolic Execution
p.2314

Research on the Image Detection of Moving Objects in Natural Environment
p.2318

Research on the Relationship between Projection Number and Image Noise Level in FBP Reconstruction
p.2324

An Improved Mean Shift Algorithm for Vehicle Tracking
p.2329

Moving Target Tracking Using Sparse Optical Flow Method
p.2335

A New Fast Computation Method in Mean Filter
p.2340

A Method to Extract Contour of Wheat Pests Based on LCV Model
p.2344

Face Recognition Method Based on Multi-Level Histogram Sequence of Gabor Fused Features
p.2348

Artificial Immune Network in the Recognition of Yunnan Herbal Medicine
p.2353

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 718-720Moving Target Tracking Using Sparse Optical Flow...

Moving Target Tracking Using Sparse Optical Flow Method

Abstract:

Moving target tracking is a hot research spot of computer vision and applied in various fields. In this paper, a new tracking method base on sparse optical flow is put forward. In this method, targets are tracked through calculating the movements of Harris corner points, rather than the movements of all pixel points. Experiments results show that the tracking effect of this new method is pretty good. Tracking accuracy can reach more than 80% in most experimental conditions. And according to other peoples research production, experiments based on dense optical flow are done to compare with the new method proposed in this paper. The comparison results show that the new method has high calculation efficiency. This indicates that the method has feasibility and practical value.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 718-720)

Pages:

2335-2339

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.718-720.2335

Citation:

Cite this paper

Online since:

July 2013

Authors:

Tian Ding Chen, Jian Hu, Chao Lu, Zhong Jiao He

Keywords:

Dense Optical Flow, Fast Calculation, High Tracking Accuracy, Moving Target Tracking, Sparse Optical Flow

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Song L, Huang Y L, Shen L S. Summary of Video Monitoring System [J]. Measurement and control technology, 2003, 22(5): 33-35.

Google Scholar

[2] Tu D W, Jiang J L. Improved Algorithm for Motion Image Analysis Based on Optical Flow and its Application. Optics and Precision Engineering, 2011, 19(5): 1159-1164.

DOI: 10.3788/ope.20111905.1159

Google Scholar

[3] Chu J, Shi M, Fu X. Detection of Moving Target in Dynamic Background Based on Optical Flow. Journal of Nanchang Hangkong University (Natural Sciences), 2011, 25(3): 1-6.

Google Scholar

[4] Huang Z, Xu Y, et al. Method for Opposing Flow Abnormal Event Detection Based on Optical Flow [J]. Video application & project, 2011, 35(13):102-105.

Google Scholar

[5] B.K.P. Horn, B.G. Schunck. Determining Optical Flow [J]. Artificial Intelligence, 1981, 17:185-203.

DOI: 10.1016/0004-3702(81)90024-2

Google Scholar

[6] Gary Bradski, Adrian Kaebler. Learning OpenCV [M]. USA: O'Reilly Media, Inc., 2009: 1-2.

Google Scholar

[7] B.Lucas, T.Kanade. An Iterative Image Registration Technique with an Application to Stereo Vision[C]. //Proceedings of DARPA Image Understanding WorkShop, 1981, 121-130.

Google Scholar

[8] C.Harris, M.Stephens. A Combined Corner and Edge Detector[C]. //Proceedings of the 4th Alvey Vision Conference, 1988, 147-151.

DOI: 10.5244/c.2.23

Google Scholar

[9] Tao L. Image Information Extraction based on Optical Flow Technology [D]. Wuhan: Huazhong University of Science and Technology, 2005.

Google Scholar