Deep learning based super-resolution for improved action recognition

Abstract

Action recognition systems mostly work with videos of proper quality and resolution. Even most challenging benchmark databases for action recognition, hardly include videos of low-resolution from, e.g., surveillance cameras. In videos recorded by such cameras, due to the distance between people and cameras, people are pictured very small and hence challenge action recognition algorithms. Simple upsampling methods, like bicubic interpolation, cannot retrieve all the detailed information that can help the recognition. To deal with this problem, in this paper we combine results of bicubic interpolation with results of a state-ofthe-art deep learning-based super-resolution algorithm, through an alpha-blending approach. The experimental results obtained on down-sampled version of a large subset of Hoolywood2 benchmark database show the importance of the proposed system in increasing the recognition rate of a state-of-the-art action recognition system for handling low-resolution videos.

A Survey on Deep Learning Based Approaches for Action and Gesture Recognition in Image Sequences

“…Immediate effort is also expected in action/gesture localization in long, untrimmed, and realistic videos [128,34,95]. As such, we envision newer problems like early recognition [28], multi-task learning [127], captioning, recognition from low resolution sequences [66] and lifelog devices [87] will receive attention in the next years.…”

Deep Learning for Action and Gesture Recognition in Image Sequences: A Survey

“…Additional effort is expected to advance in the research of methods able to simultaneously perform both detection and recognition tasks in long, realistic videos (Gkioxari and Malik, 2015;Shou et al, 2016b). As such, we envision other related problems like early recognition Escalante et al (2016a), multi task learning , captioning, recognition from low resolution sequences Nasrollahi et al (2015) and from lifelog devices Rhinehart and Kitani (2016) will receive special attention within the next few years.…”

Emotion Recognition from Skeletal Movements

“…Skeleton tracking has become much easier with the appearance of motion capture systems, which automatically generate the human skeleton represented by 3-dimensional (3D) coordinates. Additionally, it brought up an increase of research on body movement, such as unusual event detection and crime prevention [ 16 , 17 , 18 , 19 , 20 ].…”