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Technical Program now available
Author notification sent out. (updated on 07/10/2013).
The submission deadline is extended to 15/09/2013. (updated on 07/09/2013).
Speaker: Dr. Zhengyou Zhang, IEEE Fellow, Microsoft, Redmond
Biography
Zhengyou Zhang is a Fellow of the Institute of Electrical and
Electronic Engineers (IEEE). He is the Founding Editor-in-Chief of the
newly established IEEE Transactions on Autonomous Mental Development
(IEEE T-AMD), and is on the Editorial Board of the International
Journal of Computer Vision (IJCV), the Machine Vision and
Applications, and the Journal of Computer Science and Technology
(JCST). He was on the Editorial Board of the IEEE Transactions on
Pattern Analysis and Machine Intelligence (IEEE T-PAMI) from 1999 to
2005, the IEEE Transactions on Multimedia (IEEE T-MM) from 2004 to
2009, the International Journal of Pattern Recognition and Artificial
Intelligence (IJPRAI) from 1997 to 2008, among others. He is listed in
Who's Who in the World, Who's Who in America and Who's Who in
Science and Engineering.
Technical Program (Date: 08 Dec. 2013,
Location: Room 105)
8:30 |
Opening Remarks |
8:35-9:20 |
Keynote Speech
Speaker: Dr. Zhengyou Zhang, IEEE Fellow, Microsoft, Redmond
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| 9:20-10:10 |
Session I
Spatio-Temporal Context Modeling for BoW-Based Video
Classification
Saehoon Yi (Rutgers University), Vladimir Pavlovic (Rutgers University)
Semantic Video-to-Video Search using Sub-Graph Grouping and Matching
Tae Eun Choe (ObjectVideo Inc.), Hongli Deng (ObjectVideo Inc.),
Feng Guo (Google Inc.), Mun Wai Lee (Intelligent Automation,
Inc.), Niels Haering (ObjectVideo Inc.)
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| 10:10-10:30 | Morning Coffee Break |
| 10:30-11:45 |
Session II
NSH: Normality Sensitive Hashing for Anomaly Detection
Hirotaka Hachiya (Canon INC.), Masakazu Matsugu (Canon INC.)
Dynamic Scene Classification using Spatial and Temporal Cues
Arun Balajee Vasudevan (IIT Jodhpur), Srikanth
Muralidharan (IIT Jodhpur), Pratheek Chintapalli (IIT Jodhpur),
Shanmuganathan Raman (IIT Gandhinagar)
VGRAPH: A Novel Approach for Generating Static Video
Summaries using Nearest Neighbor Graph
Karim Mahmoud (IBM,Alexandria University)
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11:45 |
Concluding Remarks |
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.)
- Submission Deadline
Sept. 7th, 2013 Extended to Sept.15th, 2013
- Notification of Acceptance Oct. 7th, 2013
- Camera-Ready Submission Oct. 13th, 2013
- Workshop Dec. 8th, 2013
- 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
- Anurag Bhardwaj, eBay Research Lab
- Rama Chellappa, University of Maryland, USA
- Mert Dikmen, UIUC, USA
- GianLuca Foresti, University of Udine, Italy
- Jordi Gonzalez, UAB, Spain
- Jungong Han, Civolution
- Ran He, Chinese Academy of Sciences, China
- Yu Gang Jiang, Columbia University, USA
- Graeme Jones, Kingston University, UK
- Dmitry Kit, Northeastern University, USA
- Xuelong Li, Chinese Academy of Sciences, China
- Ioannis Patras, Queen Mary University of London, UK
- Ling Shao, University of Sheffield, UK
- Yan Song, University of Science and Technology of China
- Liang Wang, Chinese Academy of Science, China
- Tao Xiang, Queen Mary University of London, UK
- Yun Fu, Northeastern University, Boston, USA
- Zhang Zhang, Chinese Academy of Sciences, China
- Jianguo Zhang, University of Dundee, UK
- Xu Zhao, Shanghai Jiaotong University, China
- 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 ICCV 2013 main conference and VECTaR'13 is allowed.
- The format of a paper submission is the same as the ICCV main conference. Please follow instructions on the ICCV 2013 website http://www.iccv2013.org/author_guidelines.php.
- For the paper submission, please go to the Submission Website (https://cmt.research.microsoft.com/VECTAR2013/)
- All accepted papers will appear in the ICCV-IEEE Xplore. There will be a charge of a flat $200 fee for each accepted paper. At least one of the authors must register for the workshop and present the paper.
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 ICCV 2013 (included in the main conference DVD and in IEEE Xplore).
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