Automatic Detection of Crucial Areas Based on Speed Correlation in Video Sequences

Shuang Jun Liu; Rong Yi Cui

doi:10.4028/www.scientific.net/AMM.427-429.1789

Paper Titles

Based on Wavelet Transform of TDOA/AOA Location Tracking Algorithm
p.1772

Dense 3D Reconstruction Based on Photometric Stereo with Unknown Light Source via Energy Minimization Framework
p.1776

A New Image Cipher Algorithm Based on Chaos
p.1781

Identification of Soil Stabilizer Noise Source with Partial Coherence Analysis
p.1785

Automatic Detection of Crucial Areas Based on Speed Correlation in Video Sequences
p.1789

Research on Improved Code Phase Acquisition Algorithm for BOC(pn,n) Signal
p.1794

Fault Diagnosis Method for Network Control Systems Applying SVM Disturbance Compensation
p.1799

Finding the Pure-Strategy Nash Equilibrium Using a Fixed-Point Algorithm
p.1803

A Novel Image Fusion Approach for Underwater Blurred Images
p.1807

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 427-429Automatic Detection of Crucial Areas Based on...

Automatic Detection of Crucial Areas Based on Speed Correlation in Video Sequences

Abstract:

Based on video frame differential optical flow field, a method of crucial area detection for surveillance video images of examination room is proposed in this paper. Firstly, the optical flow field was calculated with the difference between two adjacent frames. Secondly, the scene was divided roughly into several blocks, and the blocks of which centroid speed is higher than given threshold were further divided into fine sub-blocks, and furthermore, the sub-block which has maximum centroid speed in the block was marked as the area of abnormal target. Finally, the sub-blocks with exceptional speed in the same observation time slice were judged to be the correlate areas with abnormal speed (CAAS), and the intersection of adjacent CAAS were determined as the crucial area. Experimental results show that the proposed method can effectively detect the abnormal movement area, and can accurately position the crucial area affecting other targets movement.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 427-429)

Pages:

1789-1793

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.427-429.1789

Citation:

Cite this paper

Online since:

September 2013

Authors:

Shuang Jun Liu*, Rong Yi Cui

Keywords:

Abnormal Target Detection, Crucial Area, Optical Flow Field, Surveillance of Examination Room

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Guo Qing Yin, Dietmar Bruckner and Gerhard Zucker: IEEE AFRICON 2009 Vol. 23-25 (Nairobi, Kenya 2009).

Google Scholar

[2] L. Havasi and Z. Szlavik: Proceedings of 2010 IEEE 17th International Conference on Image Processing Vol. 26-29 (Hong Kong, 2010).

Google Scholar

[3] Ting Rui, Qi Zhang, You Zhou and Jianchun Xing: Neurocomputing (2013).

Google Scholar

[4] A. Ziaei, S. M. Ahadi and H. Yeganeh: ICSP2008 Proceedings.

Google Scholar

[5] T. Lim, B. Han and J. H. Han: Pattern Recognition Vol. 45 (2012), pp.1696-1706.

Google Scholar

[6] N. A. Mandellos, I. Keramitsoglou and C. T. Kiranoudis: Expert Systems with Applications Vol. 38 (2011), pp.1619-1631.

DOI: 10.1016/j.eswa.2010.07.083

Google Scholar

[7] Yanzhu Zhang, Xiaoyan Wang and Biao Qu: Procedia Engineering Vol. 29 (2012), pp.2705-2709.

Google Scholar

[8] Shui-gen Wei, Lei Yang, Zhen Chen and Zhen-feng Liu: Procedia Engineering Vol. 15 (2011), pp.3471-3476.

Google Scholar

[9] H. A. Rashwan, D. Puig and M. A. Garcia: Computer Vision and Image Understanding Vol. 116 (2012), pp.953-966.

DOI: 10.1016/j.cviu.2012.04.006

Google Scholar

[10] B. D. Lucas and T. Kanade: Proceedings of Imaging Understanding Workshop Vol. 121~130 (1981).

Google Scholar

[11] B. K. P. Horn and B. G. Schunck: Artificial Intelligence Vol. 17(1/3) (1981), p.185~203.

Google Scholar