Action Recognition Using Local Spatio- Temporal Oriented Energy Features and Additive Kernel SVMs

Spatio-temporal oriented energy features have been proved to be an efficient feature for action recognition. It has satisfied performance on most of public databases. However, the oriented energy features were used as holistic action features for template matching in many literatures. In the paper, we proposed an action representation based on local spatio-temporal oriented energy features, and multiple feature channels are built to convert the features to descriptors. Moreover, inspired by additive kernel Support Vector Machine can offer significant improvements in accuracy on a wide variety of tasks while having the same run-time. We proposed action classifiers based on additive kernels and tested our system on KTH human action dataset for its performance evaluation. The experimental result shows our system outperforms most of recent action classification systems. 