Special Issue "Human Activity Recognition Based on Image Sensors and Deep Learning"
Deadline for manuscript submissions: 21 May 2021.
Interests: virtual and augmented reality; computer vision; image processing
Video-based human activity recognition (HAR) has made considerable progress in recent years due to its applications in various fields, such as surveillance, entertainment, smart homes, sport analysis, human–computer interaction, virtual reality, enhanced manufacturing, and healthcare systems. Its purpose is to automatically detect, track, and describe human activities in a sequence of image frames.
Deep learning (DL) techniques have become popular for video-based HAR, thanks in particular to their accuracy and their ability to handle large and well-annotated video databases. Nonetheless, their application to this field is still relatively new. Consequently, the exploration of use of DL in video-based HAR provides scope for significant contributions. For example, common DL approaches automatically extract hierarchical features from static images and do not take into account motion, which is a key feature for human activity description. Using techniques such as long short-term memory (LSTM), which have proven their power in motion modeling, could provide more efficient solutions. Moreover, multistream networks would allow modeling the temporal dependencies between motion and appearance. To deal with challenging conditions (such as background cluttering and illumination change) introduced by the motion in images and improve classification performance, advanced DL architectures could be considered like transfer learning, generative adversarial network (GAN), and multitask learning.
The aim of this Special Issue is to report on recent research works on video-based human activity recognition using advanced deep learning techniques. We encourage submissions of conceptual, empirical, and literature review papers focusing on this field, regardless of the application area.
Prof. Dr. Fakhreddine Ababsa
Dr. Cyrille Migniot
Manuscript Submission Information
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