  Temporal feature extraction is an important issue in video-based action
recognition. Optical flow is a popular method to extract temporal feature,
which produces excellent performance thanks to its capacity of capturing
pixel-level correlation information between consecutive frames. However, such a
pixel-level correlation is extracted at the cost of high computational
complexity and large storage resource. In this paper, we propose a novel
temporal feature extraction method, named Attentive Correlated Temporal Feature
(ACTF), by exploring inter-frame correlation within a certain region. The
proposed ACTF exploits both bilinear and linear correlation between successive
frames on the regional level. Our method has the advantage of achieving
performance comparable to or better than optical flow-based methods while
avoiding the introduction of optical flow. Experimental results demonstrate our
proposed method achieves the state-of-the-art performances of 96.3% on UCF101
and 76.3% on HMDB51 benchmark datasets.
