Deep network for extremely low-resolution human action recognition

Abstract

Due to advancement in automated applications, privacy-preserving is an emerging concern. This concern is more significant in the case of human-centred surveillance application like human action recognition (HAR). Along with privacy concern, the computational complexity due to the huge size of video data is another major concern. To overcome these limitations, an attempt is made to examine the domain of human action recognition in low-resolution (LR) videos. The extremely LR video data ensures sufficient distortion in visual information to hide the identity of the person. Therefore, working with LR videos can resolve the above mentioned concerns of privacy preserving and computational complexity up to a certain extent. In this paper, a new generative adversarial network (GAN) based neural architecture is proposed for HAR in extremely low-resolution videos. The extensive results analysis with ablation study on the state-of-the-art datasets proves the effectiveness of the proposed method over the existing methods for LR-HAR.