On the Effects of Low Video Quality in Human Action Recognition

Human activity recognition is one of the most intensively studied areas of computer vision and pattern recognition in recent years. A wide variety of approaches have shown to work well against challenging image variations such as appearance, pose and illumination. However, the problem of low video quality remains an unexplored and challenging issue in real-world applications. In this paper, we investigate the effects of low video quality in human action recognition from two perspectives: videos that are poorly sampled spatially (low resolution) and temporally (low frame rate), and compressed videos affected by motion blurring and artifacts. In order to increase the robustness of feature representation under these conditions, we propose the usage of textural features to complement the popular shape and motion features. Extensive experiments were carried out on two well-known benchmark datasets of contrasting nature: the classic KTH dataset and the large-scale HMDB51 dataset. Results obtained with two popular representation schemes (Bag-of-Words, Fisher Vectors) further validate the effectiveness of the proposed approach.