Deep Learning Applications for Pose Estimation and Human Action Recognition—2nd Edition

Dear Colleagues,

In the last decade, deep learning has drawn significant attention thanks to its robustness and potential in generalization and learning capabilities. Several applications have been tested and successfully deployed, exploring the majority of real-world tasks with the aim of improving their performances. Among others, pose estimation and human action recognition have benefitted from the exceptional results achieved in the deep learning field, although still showing wide margins of improvement.

This Special Issue aims to gather a significant collection of original contributions to these topics. Accurate vehicle and human pose estimation is crucial for several applications, e.g., animal behavior research, gaming and virtual reality, medicine and biotechnology, pedestrian, aerial and maritime navigation, robotics, and human motion tracking. Furthermore, effective human pose and action recognition offers an important contribution in many fields, such as physical therapists’ diagnoses and patient rehabilitation, as well as security and surveillance or employee-free store development.

The relevant topics of this issue include, but are not limited to, the following:

• Single and multihuman pose estimation, action recognition, and tracking;
• Terrestrial, maritime, aerial robot pose estimation, and tracking;
• Literature reviews and surveys;
• Datasets and sensors;
• Interesting applications and ideas focusing on surveillance, autonomous navigation, human–robot interaction, healthcare, and sports, etc.

Dr. Paolo Russo
Dr. Fabiana Di Ciaccio
Guest Editors

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