VECTaR2014

The goal of this workshop is to provide a forum for recent research advances in the area of video event categorization, tagging and retrieval, particularly with the increasing BIG volume of video data. The workshop seeks original high-quality submissions from leading researchers and practitioners in academia as well as industry, dealing with theories, applications and databases of visual event recognition. Topics of interest include, but are not limited to:

Big video event database gathering and annotation
A large scale dataset benchmarking
Deep learning for large scale event recognition
Event detection in big social media
Event recognition with depth cameras
Multi-modal and multi-dimensional event recognition
Multi-spectrum data fusion
Spatial temporal features for event categorization
Hierarchical event recognition
Probabilistic graph models for event reasoning
Global/local event descriptors
Metadata construction for event recognition
Event-based video segmentation and summarization
Efficient indexing and concepts modeling for video event retrieval
Semantic-based video event retrieval
Online video event tagging

We will select the paper with excellent technical contributions and broad impact for Best Paper Award.

Aims and Scope

With the vast development of Internet capacity and speed, as well as wide usage of media technologies in people's daily life, it is highly demanding to efficiently process or organize video events rapidly emerged from the Internet (e.g., YouTube), wider surveillance networks, mobile devices, smart cameras, depth cameras (e.g., kinect)etc. The human visual perception system could, without difficulty, interpret and recognize thousands of events in videos, despite high level of video object clutters, different types of scene context, variability of motion scales, appearance changes, occlusions and object interactions. For a computer vision system, it has been very challenging to achieve automatic video event understanding for decades. Broadly speaking, those challenges include robust detection of events under motion clutters, event interpretation under complex scenes, multi-level semantic event inference, putting events in context and multiple cameras, event inference from object interactions, etc.

In recent years, steady progress has been made towards better models for video event categorization and recognition, e.g., from modeling events with bag of spatial temporal features to discovering event context, from detecting events using a single camera to inferring events through a distributed camera network, and from low-level event feature extraction and description to high-level semantic event classification and recognition. However, the current progress in video event analysis is still far from its promise. It is still very difficult to retrieve or categorize a specific video segment based on their content in a real multimedia system or in surveillance applications. The existing techniques are usually tested on simplified scenarios, such as the KTH dataset, and real-life applications are much more challenging and require special attention. To advance the progress further, we must adapt recent or existing approaches to find new solutions for intelligent large scale video event understanding.

Important Dates

Paper Submission: ~~June 20, 2014~~ July 2, 2014
Notification of acceptance: July 18 , 2014
Pre-Paper for USB Stick: July 25, 2014
Camera-Ready paper due: Aug 8 2014
Workshop: 6th September 2014

General Chairs

Prof. Thomas S. Huang, University of Illinois at Urbana-Champaign, USA
Prof. Tieniu Tan, Chinese Academy of Sciences, China

Program Chairs

Dr. Yun Raymond Fu, Northeastern University, Boston, USA
Dr. Ling Shao, The University of Sheffield, UK
Dr. Jianguo Zhang, University of Dundee, UK
Dr. Liang Wang, Chinese Academy of Sciences, China

Program Committee

Aggelos K. Katsaggelos, Northwestern University, USA
Graeme Jones, Kingston University, UK
Shiguang Shan, Chinese Academy of Sciences, China
Charles Dyer, University of Wisconsin - Madison, USA
Junsong Yuan, Nanyang Technological University, Singapore
Baoxin Li, Arizona State University, USA
Gian Luca Foresti, University of Genoa, Italy
Avinash Kak, Purdue University, USA
Sungjoo Suh, Samsung, South Korea
Zicheng Liu, Microsoft Research, USA
Fatih Porikli, Australian National University, Australia
Xueming Qian, Xi'an Jiaotong University, China
Rama Chellappa, University of Maryland, USA
Lucio Marcenaro, University of Genoa, Italy
Ming Shao, Northeastern University, USA
Vittorio Murino, Istituto Italiano di Tecnologia & University of Verona, Italy
Jinjun Wang, Xi'an Jiaotong University, China

Submission

When submitting manuscripts to this workshop, the authors acknowledge that manuscripts substantially similar in content have NOT been submitted to another conference, workshop, or journal. However, dual submission to the ECCV 2014 main conference and VECTaR'14 is allowed.
The format of a paper submission is the same as the ECCV main conference. Please follow instructions on the ECCV 2014 website http://eccv2014.org/author-instructions/.
For the paper submission, please go to the Submission Website (https://cmt.research.microsoft.com/VECTAR2014/)

Review

Each submission will be reviewed by at least three reviewers from program committee members and external reviewers for originality, significance, clarity, soundness, relevance and technical contents. Accepted papers will be published together with the proceedings of ECCV 2014.

Contacts

Dr. Yun Raymond Fu, yunfu@ece.neu.edu
Dr. Ling Shao,ling.shao@sheffield.ac.uk
Dr. Jianguo Zhang, jgzhang@computing.dundee.ac.uk
Dr. Liang Wang, wangliang@nlpr.ia.ac.cn

The 6th International Workshop on Video Event Categorization, Tagging and Retrieval towards Big Data ( VECTaR2014 ) , September 6th, 2014

In Conjunction with ECCV 2014

Keynote Speakers

Technical Program

Call for Papers