Modeling Human Behavior for Video Surveillance Using Geometric Constraints Pranav Mantini Advisor: Dr. Shishir Shah 1
Content Introduction Construct Geometric Models Build Accessibility Distribution – Feature Extraction – Classification Results Current Experiments Future Work 2
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] 3
“Surveillance” 4
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 5
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 6
Construct Geometric Models Building from Floor Plans using SketchUp OpenGL Rendering from Mesh Export as Collada 7
Embedding Virtual Cameras and Calibration Store in COLLADA File along with geometry 8
Accessibility Distribution Delaunay Triangulations Accessibility Distribution 9
Standard Features Representation for floor vertices Characteristics – Indifferent to geometry – Rotational and scale invariance Theory of proxemics 10
Classification Results Train a multilayer neural network 11
Four Category Case 12
Classification Results Gaussian Process Classiffier 13
Future Work Estimate or predict human trajectories by using the subjects initial parameters and building a vector field from accessibility distribution. 14
References [1]. Lyon, David 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. 15