Apologies for cross-posting.
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Pattern Recognition: special issue on "Sparse representation for event
recognition in video surveillance",
Submission deadline: March 15, 2012
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Introduction
Understanding and interpreting object behaviours based on video analysis has
witnessed tremendous progress in the last twenty years. However, the
performance of finished systems still needs to be significantly improved,
and many challenges remain to be solved. One of these challenges is that
there is a gap between the primitive information directly extracted from
images and semantic representations. To bridge this gap, a number of
approaches based on features (e.g. SIFT, HoG and SURF) have been reported to
address the coherence between the extracted features and their semantic
interpretations. Unfortunately, due to feature redundancy and complexity,
these primitive features do not always lead to consistent or semantically
meaningful representations.
In recent years, sparse signal representation has proven to be an effective
tool for representing and compressing images. It has been demonstrated that
optimal decomposition of the signals using appropriately designed bases
(e.g. wavelets, Fourier coefficients or spatio-temporal words) can lead to
less ambiguity/uncertainty and computational complexity in image
interpretation and representation. In spite of its success, sparsity
analysis still has limitations in terms of computational efficiency and
interpretation accuracy. As a result, continuous efforts are currently being
made towards adaptive sparsity transformations of morphological data such as
extracted events in video surveillance.
The primary purpose of this special issue is to organise a collection of
recently developed analysis techniques based on sparse representations for
understanding/interpreting video analysis of object behaviours. This
includes object detection and tracking, segmentation, spatial and temporal
features extraction, human body modelling and synthesis, event recognition,
behaviour learning and applications. This special issue is intended to be an
international forum for researchers to report the recent developments in
this field in an original research paper style.
Topics
The topics include, but are not limited to:
l Sparse representation for event search and retrieval
l Sparse learning in motion trajectory analysis
l Object detection and tracking using sparse inference
l Sparse representation for crowd behaviour analysis
l Occlusion and segmentation errors handling
l Sparse representation for 2D/3D articulated human body modelling
l Sparsity analysis for modelling and learning object behaviours
l Sparse interpretations of object behaviours
Paper submission
Before submission, authors should carefully read over the journal's Author
Guidelines, which are located at:
<http://www.elsevier.com/wps/find/journaldescription.cws_home/328/authorinst
ructions>
http://www.elsevier.com/wps/find/journaldescription.cws_home/328/authorinstr
uctions.
Prospective authors should submit an electronic copy of their complete
manuscript (6-15 pages in the Pattern Recognition publication format)
through the journal manuscript tracking system at the web site:
<http://ees.elsevier.com/pr/> http://ees.elsevier.com/pr/, indicating that
their contribution is for the special issue "Sparse representation for event
recognition in video surveillance." Submitted manuscripts will be reviewed
according to the peer review policy of Pattern Recognition.
Important Dates
Manuscript Due
March 15, 2012
First Round of Reviews
June 15, 2012
Second round of Reviews
September 1, 2012
Notification of Acceptance
September 15, 2012
Tentative Publication Date
(Approx.) Mid-year 2013
Guest Editors
* Huiyu Zhou, PhD, Institute of Electronics, Communications and
Information Technology, Queen's University Belfast, United Kingdom. E-mail:
<mailto:[log in to unmask]> [log in to unmask]
* Jianguo Zhang, PhD, School of Computing, University of Dundee,
United Kingdom. E-mail: <mailto:[log in to unmask]>
[log in to unmask]
* Liang Wang, PhD, National Lab of Pattern Recognition, Beijing, China.
E-mail: <mailto:[log in to unmask]> [log in to unmask]
* Zhengyou Zhang, PhD, Microsoft Research, Microsoft Corp., One
Microsoft Way, Redmond WA, USA. E-mail: <mailto:[log in to unmask]>
[log in to unmask]
* Lisa M. Brown, PhD, IBM Thomas J. Watson Research Center, Hawthorne,
NY USA. E-mail: <mailto:[log in to unmask]> [log in to unmask]
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