Moving Object Detection Based on Bionic Compound Eye

Article Preview

Abstract:

In view of the traditional image difference method for camera movement under the condition of low accuracy on moving target detection, the image difference method based on bionic compound eye moving target detection algorithm was proposed and optimized. It used the image difference method based on bionic compound eye to detect the moving targets, and optimize the result by pixel interpolation and the ratio. The experimental conclusions show that the accuracy about new motion detection method is 25% higher than the traditional method.

You might also be interested in these eBooks

Info:

Periodical:

Advanced Materials Research (Volumes 926-930)

Pages:

3563-3567

Citation:

Online since:

May 2014

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Salma Kammoun Jarraya, Mohamed Hammami and Hanene Ben-Abdallah, Accurate Background Modeling For Moving Object Detection in a Dynamic Scene . Digital Image Computing, 2010: 1-6.

DOI: 10.1109/dicta.2010.18

Google Scholar

[2] Wenhui Chen, Jing Zhang. A moving object detection based on background subtraction and frame difference algorithm, Electronic Design Engineering, 2013, 21, (3): 24-26.

Google Scholar

[3] Xin Jin, Xuechun Liang. Statistical techniques of multiple target tracking under complex conditions. Computer science 2013, 40(6): 268-271.

Google Scholar

[4] Jing Xu, HeZhang, Xiangjin Zhang. Interframe difference and optical flow method of infrared image based motion detection. The computer simulation, 2012, 29(6): 248-252.

Google Scholar

[5] Rita Cucchiara. Detecting Moving Objects, Ghosts, and Shadows in Video Streams. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2003, 25(10): 1337-1342.

DOI: 10.1109/tpami.2003.1233909

Google Scholar

[6] LiangfenWang. Background model based on SIFT feature matching and dynamic update of moving target detection algorithm. Journal of computer applications and software, 2010, 27(2): 267-270.

Google Scholar

[7] Andrew D. Straw, Eric J. Warrant, and David C. O'Carroll. A bright zone, in male hoverfly (Eristalis tenax) eyes and associated faster motion detection and increased constrast sensitivity . The Jounal of Experimental Biology, 2006, 209: 4339-4354.

DOI: 10.1242/jeb.02517

Google Scholar

[8] Gao peng qi, YanLei. UAV bionic compound eye motion target detection mechanism and method research. Beijing university doctoral dissertation, (2009).

Google Scholar

[9] Karin Nordstrom, Paul D. Barnett, David C. O'Carroll. Insect Detection of Small Targets Moving in Visual Clutter. PLoS Biol, 2006, 4: 378-386.

Google Scholar

[10] David G. Distinctive Image Features from Scale-Invariant Keypoints Lowe. Computer Science Department University of British Columbia. 2004 Vancouver, B.C., Canada.

Google Scholar

[11] TingXu. Automatic remote sensing image registration algorithm based on SIFT. Mechanical and electrical engineering, 2013, 30 (1): 111-115.

Google Scholar

[12] Dong Wang, Yi Xia. Image matching algorithm based on SIFT features. Computer and digital engineering, 2013(3): 477-47.

Google Scholar

[13] Cheng Chen, Yue Ting Zhuang, Jun Xiao. Video foreground extraction under the condition of Camera movement. Journal of zhejiang university, 2009. 43(6): 973-977.

Google Scholar