Modeling human behavior for video surveillance using geometric constraints
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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

Modeling Human Behavior for Video Surveillance Using Geometric Constraints

PranavMantini

Advisor: Dr. Shishir Shah


Content
Content

  • Introduction

  • Construct Geometric Models

  • Build Accessibility Distribution

    • Feature Extraction

    • Classification Results

  • Current Experiments

  • Future Work


Surveillance
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]



Automated video surveillance
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


Construct geometric models
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


Construct geometric models1
Construct Geometric Models

Export as Collada

Building from Floor Plans using SketchUp

OpenGL Rendering from Mesh


Embedding virtual cameras and calibration
Embedding Virtual Cameras and Calibration

Store in COLLADA File along with geometry

Extract Transformation Matrix


Accessibility distribution
Accessibility Distribution

  • Delaunay Triangulations

  • Accessibility Distribution


Standard features
Standard Features

  • Representation for floor vertices

  • Characteristics

    • Indifferent to geometry

    • Rotational and scale invariance

  • Theory of proxemics


Classification results
Classification Results

  • Train a multilayer neural network



Classification results1
Classification Results

  • Gaussian Process Classiffier


Future work
Future Work

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


References
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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