Motion Saliency Detection in Videos

Quan Quan Wan

doi:10.4028/www.scientific.net/AMM.644-650.4603

Paper Titles

Design of Reconfigurable Carrier NCO and PN Code NCO in Satellite Navigation Receiver
p.4587

Real-Time Positioning Accuracy Analysis of Single-Frequency GPS Based on Ionosphere Grid Products
p.4591

Demodulating Method of Frequency Predictable Digital FM Signal
p.4595

The Application of a Linear FM Interference Suppression in DSSS Technology Systems
p.4599

Motion Saliency Detection in Videos
p.4603

A Novel Multi-Timescales Layered Intention Recognition Method
p.4607

Design and Implementation of Large-Scale Video Tracking Software System
p.4612

Foggy Port Surveillance Video Clearness Algorithm Based on Wavelet Packet Decomposition
p.4616

Algorithm of Video Decomposition and Video Abstraction Generation Based on Face Detection and Recognition
p.4620

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 644-650Motion Saliency Detection in Videos

Motion Saliency Detection in Videos

Abstract:

In order to detect visually salient regions in video sequences, a motion saliency detection method is proposed. The motion vectors of each video frame is used to get two motion saliency features. One represents the uniqueness of the motion,and the other one represents the distribution of the motion in the video scene. Then, the Gaussian filtering is conducted to combine the two feature to make the motion saliency map, in which the salient regions or objects in the video sequences could be detected. The experimental results show that the proposed method could achieve excellent saliency detection performances.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 644-650)

Pages:

4603-4606

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.644-650.4603

Citation:

Cite this paper

Online since:

September 2014

Authors:

Quan Quan Wan*

Keywords:

Gaussian Filtering, Motion Saliency, Video, Visual Attention

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] R. Achanta, E. Estrada, P. Wils, and S. Susstrunk, in: International Conference on Computer Vision Systems (2008), p.66.

Google Scholar

[2] T. Lu, Z. Yuan, Y. Huang, D. Wu, and H, Yu, in: IEEE International Conference on Image Processing (2012), p.1081.

Google Scholar

[3] U. Rutishauser, D. Walther, C. Koch, and P. Perona, in: IEEE International Conference on Computer Vision and Pattern Recognition (2004), p.37.

Google Scholar

[4] N. Dimitrova, H. Zhang, B. Shahraray, and I Sezan: IEEE Multimedia, Vol. 9 (2012), p.42.

Google Scholar

[5] N. Dimitrova, and F. Folshani: ACM Transactions on Information Systems, Vol. 13 (1995), p.408.

Google Scholar

[6] Z. Zivkovic, in: IEEE International Conference on Pattern Recognition (2004), p.28.

Google Scholar

[7] X. Cui, Q. Liu, and D. Metaxas, in: ACM Multimedia (2009), p.617.

Google Scholar

[8] Y. Zhai, and M. Shah, in: ACM Multimedia (2006), p.815.

Google Scholar

[9] J. Harel, C. Koch, and P. Perona, in: Advances in Neural Information Processing Systems (2006), p.545.

Google Scholar

[10] R. Achanta, A. Shaji, K. Simth, A. Lucchi, P. Fua, and S. Susstrunk: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 34 (2012), p.2274.

DOI: 10.1109/tpami.2012.120

Google Scholar