Recognizing Human Actions in Low-Resolution Videos: An Approach Based on the Dempster–Shafer Theory

Abstract

To address the problem that many existing approaches are not appropriate for action recognition in low-resolution (LR) videos, this paper presents a framework based on the Dempster–Shafer (DS) theory for this purpose. In the framework, artificial neural networks (ANNs) are firstly trained for every class with training samples, and then basic belief assignments (BBAs) for underlying classes are computed with the trained ANNs. The resulted BBAs are fused from all frames in the whole video sequentially by frame-by-frame based on DS’s rule of fusion. Action recognition is last performed with a threshold-based decision making. We conducted experiments on extensive testing data with various levels of video resolution. Results reveal that the proposed framework: (1) shows outperforming recognition performances compared with state-of-the-art classifications, respectively, such as sequence matching, voting-based strategy and bag-of-words (BoW) method; and (2) can achieve a low observational latency in recognition.