An Action Classification Based on 2D2MSD

Ju Zhong; Ye Zi Sheng; Chun Li Lin; Nai Dong Cui

doi:10.4028/www.scientific.net/AMR.753-755.3064

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 753-755An Action Classification Based on 2D2MSD

An Action Classification Based on 2D2MSD

Abstract:

Double-direction two-dimensional Maximum Scatter Difference (2D2MSD) based on Maximum Scatter Difference (MSD) was proposed,which overcame the small sample size problem of LDA, and data were more concise. In the Weizmann human action database, experimental results showed the algorithm was fast, the average recognition rate reached 92% and the highest recognition rate reached 100%.

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Periodical:

Advanced Materials Research (Volumes 753-755)

Pages:

3064-3067

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.753-755.3064

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Online since:

August 2013

Authors:

Ju Zhong, Ye Zi Sheng, Chun Li Lin, Nai Dong Cui

Keywords:

Action Recognition, Double-Direction Two-Dimensional Maximum Scatter Difference (2D2MSD), Enhanced Gait Energy Image (EGEI)

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References

[1] Sminchisescu C, Kanaujia A, Li Z G etal. Proc of the Inter- national. Conference on Computer Vision. Beijing, China, ( 2005), pp.1808-1815.

Google Scholar

[2] Bobick A F, Davis JW. IEEE Trans on Pattern Analysis and Ma-chine Intelligence, Vol. 23, no. 3, (2001), pp.257-267.

Google Scholar

[3] Xianye Ben, Weixiao Meng, Rui Yan. Neurocomputing, vol. 79, 1 March 2012, Pages 173-178.

Google Scholar

[4] LIN Chun-li, WANG Ke-jun, LIYue. Journal of Computer Applications. Vol. 31(3): (2011), 721-724. (in Chinese).

Google Scholar

[5] LIN Chun-li, WANG Ke-jun, XIA Yu, CHENG Wan-sheng. Journal of Optoelectronics·Laser. Vol. 22, No. 3, (2011), pp.418-421. (in Chinese).

Google Scholar

[6] K. Fukunaga. Academic Press, New York, (1990).

Google Scholar

[7] SONG Feng-xi, CHENG Ke, YANG Jing-yu, LIU Shu-Hai. ACTA AUTOMATICA SINICA. Vol. 30, No. 6, (2004), p.890–896.

Google Scholar

[8] D. Zhang, Z.H. Zhou. Neuro computing, Vol. 69 (1–3) , (2005), p.224–231.

Google Scholar

[9] Chunli Lin, Shuxiang Guo and Kejun Wang. Proceedings of the 2010 IEEE International Conference on Information and Automation. (2010), pp.1784-1788.

Google Scholar