Advances on action recognition in videos using an interest point detector based on multiband spatio-temporal energies
This paper proposes a new visual framework for action recognition in videos, that consists of an energy detector coupled with a carefully designed multiband energy based filterbank. The tracking of video energy is performed using perceptually inspired 3D Gabor filters combined with ideas from Dominant Energy Analysis. Within this framework, we utilize different alternatives such as non-linear energy operators where actions are implicitly considered as manifestations of spatio-temporal oscillations in the dynamic visual stream. Texture and motion decomposition of actions through multiband filtering is the basis of our approach. This new energy-based saliency measure of action videos leads to the extraction of local spatio-temporal interest points that give promising results for the task of action recognition. Such interest points are processed further in order to formulate a robust representation of an action in a video. Theoretical formulation is supported by evaluation in two popular action databases, in which our method seems to outperform the state of the art.