Title

Action Mach: A Spatio-Temporal Maximum Average Correlation Height Filter For Action Recognition

Abstract

In this paper we introduce a template-based method for recognizing human actions called Action MACH. Our approach is based on a Maximum Average Correlation Height (MACH) filter. A common limitation of template-based methods is their inability to generate a single template using a collection of examples. MACH is capable of capturing intra-class variability by synthesizing a single Action MACH filter for a given action class. We generalize the traditional MACH filter to video (3D spatiotemporal volume), and vector valued data. By analyzing the response of the filter in the frequency domain, we avoid the high computational cost commonly incurred in template-based approaches. Vector valued data is analyzed using the Clifford Fourier transform, a generalization of the Fourier transform intended for both scalar and vector-valued data. Finally, we perform an extensive set of experiments and compare our method with some of the most recent approaches in the field by using publicly available datasets, and two new annotated human action datasets which include actions performed in classic feature films and sports broadcast television. ©2008 IEEE.

Publication Date

9-23-2008

Publication Title

26th IEEE Conference on Computer Vision and Pattern Recognition, CVPR

Number of Pages

-

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/CVPR.2008.4587727

Socpus ID

51949084792 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/51949084792

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