The goal of this Special Issue on Advances on Human Action, Activity and Gesture Recognition (AHAAGR) is to gather the most contemporary achievements and breakthroughs in the fields of human action and activity recognition under one cover in order to help the research communities to set future goals in these areas by evaluating the current states and trends. Especially, due to the advancement of computational power and camera/sensor technology, deep learning, there has been a paradigm shift in video-based or sensor-based research in the last few years. Hence, it is of utmost importance to compile the accomplishments and reflect upon them to reach further. This issue is soliciting original & technically-sound research articles with theoretical & practical contributions from the computer vision, machine learning, imaging, robotics, & AI communities.
Topics of interest include (but are not limited to):
Human action/activities/gesture recognition from video or other relevant sensor data
Large datasets on action/activity/gesture recognition
Multi-sensor action/activity/gesture recognition
Action/activity/gesture recognition from skeleton data, depth map.
Deep learning and action recognition
Action localization and detection; Action sequence generation/completion
Anomaly detection from surveillance videos; Action recognition in Robotics
Hand gesture recognition for virtual reality and other applications
Crowd behavior analysis and prediction from video sequences
Human behavior analysis and recognizing social interactions
Behavior recognition based on bodily & facial expressions
Applications and future trends of action/activity/gesture recognition
Submission Period (Page Length: 6~7 pg.): 6 June, 2020~ 30 July, 2020
1st Round Review Result: 15 August 2020
Revised Submission: 30 September 2020
2nd Round Review/Revision (if required): 30 October 2020
Publication: 15 November 2020
Managing Guest Editor:
Md Atiqur Rahman Ahad, Ph.D., SMIEEE (Professor, University of Dhaka; Specially Appointed Associate Professor, Osaka University).
Upal Mahbub, Ph.D. (Senior Engineer, Qualcomm Technologies Inc., San Diego, CA, USA).
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