Mid-level features

It is a great challenge to perform high level recognition tasks on videos that are poor in quality. In this paper, we propose a new spatio-temporal mid-level (STEM) feature bank for recognizing human actions in low quality videos. The feature bank comprises of a trio of local spatio-temporal features, i.e. shape, motion and textures, which respectively encode structural , dynamic and statistical information in video. These features are encoded into mid-level representations and ag-gregated to construct STEM. Based on the recent binarized statistical image feature (BSIF), we also design a new spatio-temporal textural feature that extracts discriminately from 3D salient patches. Extensive experiments on the poor quality versions/subsets of the KTH and HMDB51 datasets demonstrate the effectiveness of the proposed approach.