Call for papers: Visual human motion understanding in the Wild
Visual human motion understanding is a key computer vision task that aims at understanding the placement, trajectories, and future actions of humans in a unconstrained natural scene. This field includes the key tasks of finding people in a scene through people detection, segmentation and pose estimation, understanding movement through people tracking, and recognizing people behaviors through motion trajectories. Recently, there has been increasing interest from the academic vision community about this topic, as well as from communities in industry due to its applications in a number of fields. For example, safe mobile robot navigation, including autonomous driving depends on robots (cars) being able to recognize where nearby pedestrians are and what they might do next. Likewise, human motion understanding is key for smart surveillance, athletic performance analysis, and VR applications, human computer interaction, among others.
Although the studies of human motion understanding in computer vision are invaluable for both academia and industry, there are many fundamental problems unsolved e.g., robust object detection and tracking, unconstrained object activity recognition, etc. Recently, machine learning algorithms, such as deep learning, have been successfully applied in this area for object tracking, activity modeling and recognition and also shown promising results in some real-world applications such as human computer interaction and autonomous driving, which prepares us to exploit and develop effective machine learning algorithms for addressing fundamental issues in human motion understanding.
This special issue aims to provide an overview of the current research trends and advances being made in this field. The list of possible topics includes, but not limited to:
- Human detection in the wild
- Human pose estimation in the wild
- Human segmentation in the wild
- Human tracking in the wild
- Human behavior recognition in the wild
- Gesture Recognition in the wild
Tutorial or overview papers, creative papers outside the areas listed above but related to the overall scope of the special issue are also welcome. Prospective authors can contact the Lead Guest Editors to ascertain interest on such topics. Submission is permitted only if the paper has not been submitted, accepted, published, or copyrighted in another journal. Papers that have been published in conference and workshop proceedings may be submitted for consideration provided that (i) the authors cite their earlier work; (ii) the papers are not identical; and (iii) the journal publication includes novel elements (e.g., more comprehensive experiments). For submission information, please refer to the submission guidelines at https://asp-eurasipjournals.springeropen.com/submission-guidelines.
Initial Paper Submission: February 1, 2019
1st Review Completed: May 1, 2019
Revised Manuscript Due: August 1, 2019
2nd Review Completed: November 1, 2019
Final Manuscript Due: January 1, 2020
Publication Date: April 1, 2020
Prof. Shengping Zhang, Harbin Institute of Technology, China, firstname.lastname@example.org.
Dr. Huiyu Zhou, University of Leicester, United Kingdom, email@example.com:firstname.lastname@example.org
Prof. Xiangyuan Lan, Hong Kong Baptist University, Hong Kong, email@example.com
Dr. Lei Zhang, University of Pittsburgh, United States, LEZ37@pitt.edu
Dr. Christophoros Nikou, University of Ioannina, Greece, firstname.lastname@example.org
2017 Journal Metrics
95 days from submission to first decision
190 days from submission to acceptance
18 days from acceptance to publication
Social Media Impact
Funding your APC
- ISSN: 1687-6180