Intelligent Video Event Analysis and Understanding

1. Front Matter

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2. The Understanding of Meaningful Events in Gesture-Based Interaction

   • Radu-Daniel Vatavu

   Pages 1-19

3. Apply GPCA to Motion Segmentation

   • Hongchuan Yu, Jian J. Zhang

   Pages 21-38

4. Gait Analysis and Human Motion Tracking

   • Huiyu Zhou

   Pages 39-67

5. Spatio-temporal Dynamic Texture Descriptors for Human Motion Recognition

   • Riccardo Mattivi, Ling Shao

   Pages 69-91

6. Efficient Object Localization with Variation-Normalized Gaussianized Vectors

   • Xiaodan Zhuang, Xi Zhou, Mark A. Hasegawa-Johnson, Thomas S. Huang

   Pages 93-109

7. Fusion of Motion and Appearance for Robust People Detection in Cluttered Scenes

   • Jianguo Zhang, Shaogang Gong

   Pages 111-124

8. Understanding Sports Video Using Players Trajectories

   • Alexandre Hervieu, Patrick Bouthemy

   Pages 125-153

9. Real-Time Face Recognition from Surveillance Video

   • Michael Davis, Stefan Popov, Cristina Surlea

   Pages 155-194

10. Event Understanding of Human-Object Interaction: Object Movement Detection via Stable Changes

    • Shigeyuki Odashima, Taketoshi Mori, Masamichi Simosaka, Hiroshi Noguchi, Tomomasa Sato

    Pages 195-210

11. Survey of Dirac: A Wavelet Based Video Codec for Multiparty Video Conferencing and Broadcasting

    • Ahtsham Ali, Nadeem A. Khan, Shahid Masud, Syed Farooq Ali

    Pages 211-247

12. Erratum: Event Understanding of Human-Object Interaction: Object Movement Detection via Stable Changes

    • Shigeyuki Odashima, Taketoshi Mori, Masamichi Simosaka, Hiroshi Noguchi, Tomomasa Sato

    Pages E1-E1

13. Back Matter

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With the vast development of Internet capacity and speed, as well as wide adop- tion of media technologies in people’s daily life, a large amount of videos have been surging, and need to be efficiently processed or organized based on interest. The human visual perception system could, without difficulty, interpret and r- ognize 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 be very challenging to achieve automatic video event understanding for decades. Broadly speaking, those challenges include robust detection of events under - tion 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 categorisation and recognition, e. g. , from modelling 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. Nowadays, text based video retrieval is widely used by commercial search engines. However, it is still very difficult to retrieve or categorise a specific video segment based on their content in a real multimedia system or in surveillance applications.

• Computational Intelligence
• cognition
• computer vision
• detection
• human-computer interaction (HCI)
• intelligence
• video event analysis

• School of Computing, University of Dundee , Dundee, Scotland, UK

  Jianguo Zhang

• Department of Electronic & Electrical Engineering, The University of Sheffield, Sheffield, UK

  Ling Shao

• Microsoft Research Asia , Beijing, P.R. China

  Lei Zhang

• Digital Imaging Research Centre, Faculty of Computing, Information Systems and Mathematics, Kingston University, Surrey, UK

  Graeme A. Jones

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