VECTaR 2013 - The 5th International Workshop on Video Event Categorization, Tagging and Retrieval ( VECTaR2013 )

With the vast development of Internet capacity and speed, as well as wide adoptation 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.
The goal of this workshop is to provide a forum for recent research advances in the area of video event categorization, tagging and retrieval, in particular for depth cameras. 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:
Motion interpretation and grouping
Human Action representation and recognition
Abnormal event detection
Contextual event inference
Event recognition among a distributed camera network
Multi-modal event recognition
Multi-spectrum data fusion
Spatial temporal features for event categorization
Hierarchical event recognition
Probabilistic graph models for event reasoning
Machine learning for event recognition
Global/local event descriptors
Metadata construction for event recognition
Bottom up and top down approaches for event recognition
Event-based video segmentation and summarization
Video event database gathering and annotation
Efficient indexing and concepts modeling for video event retrieval
Semantic-based video event retrieval
Online video event tagging
Evaluation methodologies for event-based systems
Event-based applications (security, sports, news, etc.)
Important Dates
Submission Deadline Sept. 7th, 2013
Notification of Acceptance Oct. 7th, 2013
Camera-Ready Submission Oct. 13th, 2013
Workshop Dec. 8th, 2013
General Chairs
Prof. Tieniu Tan, Chinese Academy of Sciences, China
Prof. Thomas S. Huang, University of Illinois at Urbana-Champaign, USA
Program Chairs
Prof. Liang Wang, Chinese Academy of Sciences, China
Dr. Ling Shao, The University of Sheffield, UK
Dr. Jianguo Zhang, University of Dundee, UK
Dr. Yun Fu, Northeastern University, Boston, USA