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Modeling Human Behavior for Video Surveillance Using Geometric Constraints

Modeling Human Behavior for Video Surveillance Using Geometric Constraints. Pranav Mantini Advisor: Dr. Shishir Shah. Content. Introduction Construct Geometric Models Build Accessibility Distribution Feature Extraction Classification Results Current Experiments Future Work.

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Modeling Human Behavior for Video Surveillance Using Geometric Constraints

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  1. Modeling Human Behavior for Video Surveillance Using Geometric Constraints PranavMantini Advisor: Dr. Shishir Shah

  2. Content • Introduction • Construct Geometric Models • Build Accessibility Distribution • Feature Extraction • Classification Results • Current Experiments • Future Work

  3. Surveillance • “Surveillanceis the monitoring of the behavior, activities, or other changing information, usually of people for the purpose of influencing, managing, directing, or protecting”[1]

  4. “Surveillance”

  5. Automated Video Surveillance • Ultimate goal - automatically detect events that require attention[2] • Human observer is aware of 3D Geometry of the environment • Provides cues for understanding or predict human behavior • To achieve this ultimate goal, the surveillance system should have access and “understanding” of the 3D environment it is present in

  6. Construct Geometric Models • Build 3D geometry of the environment(building) by using 3D modeling tools • Google SketchUp • Maya • Blender • Dimensions and measurements obtained from existing floor plans

  7. Construct Geometric Models Export as Collada Building from Floor Plans using SketchUp OpenGL Rendering from Mesh

  8. Embedding Virtual Cameras and Calibration Store in COLLADA File along with geometry Extract Transformation Matrix

  9. Accessibility Distribution • Delaunay Triangulations • Accessibility Distribution

  10. Standard Features • Representation for floor vertices • Characteristics • Indifferent to geometry • Rotational and scale invariance • Theory of proxemics

  11. Classification Results • Train a multilayer neural network

  12. Four Category Case

  13. Classification Results • Gaussian Process Classiffier

  14. Future Work • Estimate or predict human trajectories by using the subjects initial parameters and building a vector field from accessibility distribution.

  15. References [1]. Lyon, David. 2007. Surveillance Studies: An Overview. Cambridge: Polity Press. [2]Peter H. Tu , Gianfranco Doretto , Nils O. Krahnstoever , Jens Rittscher , Thomas B. Sebastian , Ting Yu , Kevin G. Harding. An intelligent video framework for homeland protection.

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