Signal, Image and Video Processing

Special issue

Semantic representations for social behavior analysis in video surveillance systems

Call for Papers

Video surveillance has been attracting increasing attentions in the computer
vision community because of its wide industrial applications and important
scientific values. Among the related topics, automatic behavior analysis plays an
extremely important role and has witnessed tremendous progress in the last
twenty years. Recently, researchers in video surveillance shift their attention
from the monitoring of a single person’s behaviours in a relatively simple
environment to that of social behavior of multiple persons in crowded
environments. In contrast to single person’s behavior, social behavior analysis
faces more challenges such as complex interaction, diverse semantics and various
expressions. This is due to the gap between the information directly extracted
from videos and semantic interpretations by our human beings.

To bridge this gap, a number of feature representation approaches (e.g. Cuboids,
HOG/HOF, HOG3D and eSURF) have been subsequently reported to address the
coherence between the extracted features and the semantic interpretations.
Unfortunately, due to the redundancy and complexity, these hard-crafted
features may lead to diverse variations of semantic representations for social
behavior analysis.

In recent years, novel semantic representations have proven to be an effective
tool for social behavior analysis. For example, social force model and its variant
have proven to perform well in social behavior recognition. Such high-level
semantic representations achieve desired performance even if in crowded
environments. Besides, statistical approaches, syntactic approaches, and
description-based approaches also gain increasing attention in computer vision
community.

The primary purpose of this special issue is to organize a collection of recently
developed high-level semantic representations for social behavior analysis,
spreading over motion trajectory acquisition and analysis, semantic feature
extraction, social behavior analysis and applications. The 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. The topics include, but are not
limited to:

· Real-time moving object detection and tracking in crowded environments;

· Face detection and recognition in crowded environments

· 3D scene reconstruction and occlusion handling;

· Long-term trajectory clustering and analysis for social behaviors;

· Probabilistic statistical models for local semantic representation;

· Context model for global semantic representation;

· Event recognition in crowded environments;

· Abnormal behavior detection in crowded environments;

· Real-time algorithms for large scale social behavior analysis;

Schedule (tentative)

Deadline for manuscript submission: December 15, 2013
Notification of acceptance: April 15, 2014
Complete Publication Materials Due: July 15, 2014
Publication date: 2015

Submission Guidelines

Prospective authors should prepare their manuscripts according to the Signal,
Image and Video Processing guidelines
(http://www.springer.com/engineering/signals/journal/11760). Manuscripts
should be submitted via http://www.editorialmanager.com/sivp/ with article
type chosen as “S.I.: Semantic representations for social behavior analysis”.
Manuscripts should be self-contained and not exceed 30 double spaced pages
typed in 10 points or larger. All submissions will be peer reviewed for originality,
technical content and relevance to the theme of the special issue.

Guest Editors:

Shengping Zhang
Harbin Institute of Technology, China
Email: s.zhang@hit.edu.cn

Huiyu Zhou
Queen’s University Belfast, United Kingdom
Email: h.zhou@ecit.qub.ac.uk

Baochang Zhang
Beihang University, China
Email: bczhang@buaa.edu.cn

Zhenjun Han
University of Chinese Academy of Sciences, China
Email: hanzhj@ucas.ac.cn

Yuliang Guo
Brown University, United States
Email: yuliang_guo@brown.edu