Action MACH a spatio-temporal Maximum Average Correlation Height filter for action recognition

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 da...

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Mikel D. Rodriguez, Javed Ahmed, Mubarak Shah
• Keywords: Action Mach Filter, Actions Called Action, Computer Vision, CVPR 2008, Human Action Datasets, Traditional Mach Filter, Vector Valued Data