Activity recognition is a fundamental and crucial task in computer vision. Impressive results have been achieved for activity recognition in high-resolution videos, but for extreme low-resolution videos, which capture the action information at a distance and are vital for preserving privacy, the performance of activity recognition algorithms is far from satisfactory. The reason is that extreme low-resolution (e.g., 12 × 16 pixels) images lack adequate scene and appearance information, which is needed for efficient recognition. To address this problem, we propose a super-resolution-driven generative adversarial network for activity recognition. To fully take advantage of the latent information in low-resolution images, a powerful network module is employed to super-resolve the extremely low-resolution images with a large scale factor. Then, a general activity recognition network is applied to analyze the super-resolved video clips. Extensive experiments on two public benchmarks were conducted to evaluate the effectiveness of our proposed method. The results demonstrate that our method outperforms several state-of-the-art low-resolution activity recognition approaches.

Single‐Image Super‐Resolution (SISR) has always been an important topic in the field of image processing, which attempts to improve the image resolution and is of great significance in practice. Recently, SISR has made substantial progress aided by deep learning (DL), which has demonstrated impressive potential in many low‐level tasks. In the current DL‐based SISR approaches, most of them are based on supervised learning. However, in the real world, only low‐resolution (LR) images with unknown degradation are provided, which limit the application of current supervised models. To mitigate this problem, in this paper, a two‐stage semi‐supervised SISR method called SRAttentionGAN, is proposed. First, an upsampling network SRResNet, which is pre‐trained in a supervised manner, is employed to scale the LR image to the desired size. Then, the upsampled results are fed into our improved unsupervised CycleGAN framework, which does not need paired samples, to obtain sharper and more realistic super‐resolution (SR) images. Specifically, in the improved CycleGAN part, an attention‐guided generator is proposed to perceive the discriminative semantic parts between the source and target images, to avoid the impact from low‐level information. It also prevents the overall color tone from being changed. A multi‐scale discriminator is also adopted to further rich texture details. The effectiveness of the proposed SRAttentionGAN experiments is validated using four benchmarks (Set5, Set14, Urban100, and BSDS100) in both quantitative and qualitative aspects. Compared with the state‐of‐the‐arts, the results are visually promising and show competitive performance in perceptual metrics, Natural Image Quality Evaluator (NIQE) and Perception Index (PI), which have better agreement with the human visual perception.