Exploring alternative spatial and temporal dense representations for action recognition

Abstract

The automatic analysis of video sequences with individuals performing some actions is currently receiving much attention in the computer vision community. Among the different visual features chosen to tackle the problem of action recognition, local histogram within a region of interest is proven to be very effective. However, we study for the first time whether spatiograms, which are histograms enriched with per-bin spatial information, can be alternatively effective for action characterization. On the other hand, the temporal information of these histograms is usually collapsed by simple averaging of the histograms, which basically ignores the dynamics of the action. In contrast, this paper explores a temporally holistic representation in the form of recurrence matrices which capture pair-wise spatiograms relationships on a frame-by-frame basis. Experimental results show that recurrence matrices are powerful for action classification, whereas spatiograms, in its current usage, are not.