Visual Signatures in Video Visualization

Video visualization is a computation process that extracts meaningful information from original video data sets and conveys the extracted information to users in appropriate visual representations. This paper presents a broad treatment of the subject, following a typical research pipeline involving concept formulation, system development, a path-finding user study, and a field trial with real application data. In particular, we have conducted a fundamental study on the visualization of motion events in videos. We have, for the first time, deployed flow visualization techniques in video visualization. We have compared the effectiveness of different abstract visual representations of videos. We have conducted a user study to examine whether users are able to learn to recognize visual signatures of motions, and to assist in the evaluation of different visualization techniques. We have applied our understanding and the developed techniques to a set of application video clips. Our study has demonstrated that video visualization is both technically feasible and cost-effective. It has provided the first set of evidence confirming that ordinary users can be accustomed to the visual features depicted in video visualizations, and can learn to recognize visual signatures of a variety of motion events

[1]  Edsger W. Dijkstra,et al.  A Discipline of Programming , 1976 .

[2]  A. Treisman,et al.  A feature-integration theory of attention , 1980, Cognitive Psychology.

[3]  M. Posner,et al.  Attention and the detection of signals. , 1980, Journal of experimental psychology.

[4]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[5]  Editors , 1986, Brain Research Bulletin.

[6]  Z W Pylyshyn,et al.  Tracking multiple independent targets: evidence for a parallel tracking mechanism. , 1988, Spatial vision.

[7]  Tomaso A. Poggio,et al.  Motion Field and Optical Flow: Qualitative Properties , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Shadowed hedgehogs: a technique for visualizing 2D slices of 3D vector fields , 1991, Proceeding Visualization '91.

[9]  K L Shapiro,et al.  Temporary suppression of visual processing in an RSVP task: an attentional blink? . , 1992, Journal of experimental psychology. Human perception and performance.

[10]  Don Dovey Vector plots for irregular grids , 1995, Proceedings Visualization '95.

[11]  Nilesh V. Patel,et al.  Video shot detection and characterization for video databases , 1997, Pattern Recognition.

[12]  Victoria Interrante,et al.  Visualizing 3D Flow , 1998, IEEE Computer Graphics and Applications.

[13]  I. Rock,et al.  Inattentional blindness: Perception without attention. , 1998 .

[14]  Sidney S. Fels,et al.  Techniques for interactive video cubism (poster session) , 2000, ACM Multimedia.

[15]  Larry S. Davis,et al.  Monitoring human and vehicle activities using airborne video , 2000, Applied Imaging Pattern Recognition.

[16]  Sidney Fels,et al.  Techniques for Interactive Video Cubism , 2000 .

[17]  Takeo Kanade,et al.  Introduction to the Special Section on Video Surveillance , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  Ken Perlin,et al.  Painterly rendering for video and interaction , 2000, NPAR '00.

[19]  Peter-Pike J. Sloan,et al.  Video Cubism , 2001 .

[20]  P. Cavanagh,et al.  Attention-based visual routines: sprites , 2001, Cognition.

[21]  Touradj Ebrahimi,et al.  Improved linear dependence and vector model for illumination-invariant change detection , 2001, IS&T/SPIE Electronic Imaging.

[22]  Wolfgang Straßer,et al.  Interactive Visualization of Volumetric Vector Fields Using Texture Based Particles , 2002, WSCG.

[23]  G. Daniel,et al.  Video visualization , 2003, IEEE Visualization, 2003. VIS 2003..

[24]  Leonard McMillan,et al.  Proscenium: a framework for spatio-temporal video editing , 2003, ACM Multimedia.

[25]  Robert S. Laramee,et al.  The State of the Art in Flow Visualisation: Feature Extraction and Tracking , 2003, Comput. Graph. Forum.

[26]  Robert B. Fisher,et al.  The PETS04 Surveillance Ground-Truth Data Sets , 2004 .

[27]  Robert Strzodka,et al.  Real-time motion estimation and visualization on graphics cards , 2004, IEEE Visualization 2004.

[28]  Robert S. Laramee,et al.  The State of the Art in Flow Visualization: Dense and Texture‐Based Techniques , 2004, Comput. Graph. Forum.

[29]  Marcel Worring,et al.  Multimodal Video Indexing : A Review of the State-ofthe-art , 2001 .

[30]  P. Anandan,et al.  A computational framework and an algorithm for the measurement of visual motion , 1987, International Journal of Computer Vision.

[31]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[32]  Gordon Erlebacher,et al.  Overview of Flow Visualization , 2005, The Visualization Handbook.

[33]  Kristin A. Cook,et al.  Illuminating the Path: The Research and Development Agenda for Visual Analytics , 2005 .

[34]  Thomas Ertl,et al.  A Generic Software Framework for the GPU Volume Rendering Pipeline , 2005 .

[35]  Ronald A. Rensink,et al.  Change blindness: past, present, and future , 2005, Trends in Cognitive Sciences.

[36]  Min Chen,et al.  GPU-assisted Multi-field Video Volume Visualization , 2006, VG@SIGGRAPH.