Modeling Human Behavior for Video Surveillance Using Geometric Constraints Pranav Mantini Advisor:...

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

Transcript of Modeling Human Behavior for Video Surveillance Using Geometric Constraints Pranav Mantini Advisor:...

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

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Modeling Human Behavior for Video Surveillance Using Geometric Constraints

Pranav MantiniAdvisor: Dr. Shishir Shah

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Content

• Introduction• Construct Geometric Models• Build Accessibility Distribution– Feature Extraction– Classification Results

• Current Experiments• Future Work

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Surveillance

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

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“Surveillance”

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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

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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

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Construct Geometric Models

Building from Floor Plans using SketchUp

OpenGL Rendering from Mesh

Export as Collada

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Embedding Virtual Cameras and Calibration

Extract Transformation Matrix

Store in COLLADA File along with geometry

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Accessibility Distribution

• Delaunay Triangulations

• Accessibility Distribution

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Standard Features

• Representation for floor vertices

• Characteristics– Indifferent to

geometry– Rotational and scale

invariance

• Theory of proxemics

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Classification Results

• Train a multilayer neural network

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Four Category Case

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Classification Results

• Gaussian Process Classiffier

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Future Work

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

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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.