modeling human behavior for video surveillance using geometric constraints pranav mantini advisor:...
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![Page 1: Modeling Human Behavior for Video Surveillance Using Geometric Constraints Pranav Mantini Advisor: Dr. Shishir Shah 1](https://reader035.vdocuments.site/reader035/viewer/2022062309/56649dbb5503460f94aad717/html5/thumbnails/1.jpg)
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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.