“DLVA: Advances in Deep Learning and Visual Analytics for Intelligent Surveillance Systems”

in Special Issue   Posted on November 2, 2017 

Information for the Special Issue

Submission Deadline: Sun 30 Sep 2018
Journal Impact Factor : 3.255
Journal Name : Pattern Recognition Letters
Journal Publisher:
Website for the Special Issue: https://www.journals.elsevier.com/pattern-recognition-letters/call-for-papers/special-issue-on-dlva-advances-in-deep-learning-and-visual-a
Journal & Submission Website: https://www.journals.elsevier.com/pattern-recognition-letters

Special Issue Call for Papers:

The increasing sophistication and diversity of threats to public security have been calling critical demand of developing and deploying reliable, secure, and timely efficient visual intelligent surveillance systems in smart cities. For example, visual surveillance for indoor environments, like metro stations, plays an important role both in the assurance of safety conditions for the public and in the management of the transport network. When designing the next generation security solutions, it is crucial to combine sensing, computing, understanding, communication and prediction in such networked-camera systems. Examples include automated video surveillance platforms and smart camera networked systems that are monitoring the behavior, activities, or other changing information for the purpose of influencing, managing, directing, or protecting people. They exhibit a high-level of awareness beyond primitive actions, in support of persistent and long-term autonomy. However, some core problems such as object identification and tracking, and behavior analysis in intelligent surveillance are still affected by a number of practical problems. They typically involve a variety of representation, reasoning and efficiency mechanisms in the context of an extended distance and period of time and low resolution/frame rate in poor quality capturing conditions. Recent progress in computer vision techniques and related visual analytics offers new prospects for an intelligent surveillance system. A major recent development is the massive success resulting from using the deep learning techniques to enable the significant boosting of visual analysis performance and initiate new research directions to understand visual content. For example, convolutional neural networks have demonstrated superiority on modeling high-level visual concepts, while recurrent neural networks have shown promise in modeling temporal dynamics in videos. It has been and will be seen as resolution to change the whole visual recognition systems. It is expected that the development of deep learning and its related visual analytic methodologies would further influence the field of intelligent surveillance systems.

This special issue will serve a platform to publish state-of-the-art advancements in this domain of research and seeks for original contributions of work, which addresses the challenges from using deep learning and related techniques to understand and promote the ubiquitous intelligent surveillance systems. Original papers to survey the recent progress in this exciting area and highlight potential solutions to common challenging problems are also welcome. The list of possible topics includes, but not limited to:

  • Emotion/Gait/Activity/Gesture recognition and prediction
  • Large-scale video indexing
  • Pedestrian detection in the wild
  • Scene understanding and human behavior analysis
  • Person re-identification and biometric recognition
  • Summarization of long surveillance videos
  • Visual analytics for forensics and security applications
  • Pedestrian and vehicle navigation tracking
  • Face recognition and verification
  • Event (abnormal) detection and recognition
  • Cloud and distributed for visual surveillance
  • Object tracking and segmentation
  • Human computer/robot interactions
  • Data collections, benchmarking and performance evaluations

Acronym: DLVA

Important Dates

Article submission deadline: 1-30 September 2018

First round notification: 30 November 2018

Revised article submission: 31 Jan 2019

Notification of final decision: 28 Feb 2019

Submission Instructions: Papers should be prepared by adhering to the PR letters guidelines by taking into account that virtual special issue papers follow the same submission rules as regular articles. Authors should select “DLVA” assigned to the VSI as article type. Contributors must be informed that their papers should not have been published previously, nor be under consideration for publication elsewhere. If the submission are extended works of previously published papers, the original works should be included and a description of the changes that have been made should be provided. The papers accepted by this special issue is published in regular issues, the maximal length of any paper is 7 pages in the PR letters layout, as it is for regular articles. The maximum length can become 8 pages in the revised version if reviewer request significant additions. Papers submitted to this special issue should be original and technically sound. If some papers are extended versions of conference papers, the PR letter submissions should include at least 30% new contribution (more experiments, proofs of theorems not included in the conference paper, more comparisons with other methods in the literature and so on); of course the title of the PR letters paper should be different, the same figures cannot be used and the common part of the conference paper and of the extended versions cannot be verbatim the same.

Review Process:

The review process will follow the standard PR letters scheme: each paper will be reviewed by (at least) 2 referees and that, in general, only two reviewing rounds will be possible, out of which major revision is possible for the 1st round. Author should know that the papers will be most possibly rejected if after the 2nd reviewing round still need major revision.

Guest Editors

Dr. Lin Wu (Managing Guest Editor)
School of Information Technology and Electrical Engineering,
The University of Queensland, St Lucia 4072, Australia.
Email: lin.wu@uq.edu.au

Dr. Yang Wang
School of Computer Science and Engineering,
The University of New South Wales, Kensington 2052, Australia.
Email: wangy@cse.unsw.edu.au

Dr. Meng Fang
Doug McDonell Building,
The University of Melbourne, Parkville 3010 VIC, Australia
Email: meng.fang@unimelb.edu.au

Prof. Xue Li
School of Information Technology and Electrical Engineering,
The University of Queensland, St Lucia 4072, Australia.
Email: xueli@itee.uq.edu.au

Prof. Junbin Gao
Discipline of Business Analytics,
The University of Sydney Business School,
The University of Sydney, NSW 2006, Australia
Email: junbin.gao@sydney.edu.au

Guest Editors Biography

Dr. Lin Wu is Postdoctoral Research Fellow at The University of Queensland, Australia. Prior to that, she was an ARC Senior Research Fellow at School of Computer Science, The University of Adelaide, Australia. She received a PhD from School of Computer Science and Engineering, The University of New South Wales, Sydney, Australia in 2014. She has published 40 research papers in the fields of pattern recognition, machine learning and multimedia analytics, such as CVPR, ACM Multimedia, ACM SIGIR, IJCAI, IEEE TIP, IEEE TNNLS, IEEE Cybernetics, Pattern Recognition. She is the co-recipient of best research paper runner-up award for PAKDD 2014. Dr Wu has ever been the invited reviewer for more than 10 leading international journals.

Dr. Yang Wang obtained his PhD degree from the University of New South Wales, Australia, and currently serves as the research fellow from the school of Computer Science and Engineering, The University of New South Wales, Australia. He has published 35 research articles in data mining, pattern recognition and multimedia analytics area, including IEEE TNNLS, IEEE TIP, IEEE TCYB, Pattern Recognition, ACM Multimedia, ACM SIGIR, IJCAI, IEEE ICDM, ACM CIKM. Dr Wang won the best research paper runner-up award for PAKDD 2014. He was a Guest editor for Multimedia Tools and Applications and Advances in Multimedia, and served as the reviewer for more than 10 leading journals.

Dr. Meng Fang obtained his PhD degree from the University of Technology Sydney, Australia, and currently serves as a research fellow from school of computing and information Systems, The University of Melbourne, Australia. Meng has published extensively on top-tier venues, such as ACM KDD, CVPR, IEEE ICDM, AAAI, ACM CIKM, ACL, IEEE TKDE, IEEE TCYB, Data Mining and Knowledge Discovery and Neural Computation. He has won the best student paper award on ICPR 2012 in pattern Recognition and Application track.

Professor Xue Li is a Professor in the School of Information Technology and Electrical Engineering, The University of Queensland, Australia. His major areas of research interests and expertise include: Data Mining, Social Computing, Database Systems, and Intelligent Web Information Systems.

Professor Junbin Gao is the Professor of Big Data Analytics at the University of Sydney, Australia. His research interests lie in machine learning, pattern recognition and image analysis. Junbin has extensively published the research results with more than 200 research articles on the competitive venues, such as IEEE TPAMI, IEEE TNNLS, IEEE TIP, IEEE TCYB, IEEE TCSVT, IEEE TCYB, CVPR, AAAI, IJCAI, IEEE ICDM, SDM, Machine Learning, Neural Computation, Pattern Recognition and Neural Networks.

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