1. Video Based Human Motion Analysis
System for Assisted Living
PROJECT PLAN
University of Warwick
PhD Student : Faisal Azhar
Supervisor : Dr. Tardi Tjahjadi
2. Presentation layout
1. Introduction.
2. Problems and Applications.
3. Year 1 .
4. Year 2.
5. Year 3.
6. Progress to date.
1
3. Introduction
Objective
• A video based human motion analysis system for
automatic human activity recognition based on
human tracking and motion analysis .
Advantages
• Cost effective and non-sensor based.
3D Tracking
2D Tracking
2
4. Human Tracking
• Silhouette extraction from background.
• Tracking human body.
• Tracking articulated body parts.
• Recover human body pose and orientation.
Silhouette Bounding Box Articulated Tracking Body Posture
Ahmed Elgammal, 2004 Leonid Sigal, 2006 Germ´an Gonz´alez, 2006
3
5. Motion Analysis
• Derive the activities the human is performing in
front of the camera through motion analysis.
Actions as Space-Time Shapes
Lena Gorelick, 2007
3D Shape Context
Matthias Grundmann, 2008
4
6. Problems
• Generate motion primitives.
• Generating descriptors for defining an activity.
• Inference of the activity.
• Real time application in eldercare home.
Applications
• Assisted Living.
• Surveillance.
• Sports. Nils T Siebel, 2002
Michela Goffredo, 2009
5
Richard D. Green, 2004
7. Year 1 (Oct, 2010 – Sep, 2011)
Literature
Review Implement Kinematic
Tracking and write a
PG Module journal paper.
Explore, understand and
identify limitations of recent
related approaches for
tracking.
• Particle Filter.
• Extended Kalman filter. Michael Isard, 1998
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9. Year 2 (Oct, 2011 – Sep, 2012)
Project Poster
Implement an algorithm for
activity recognition.
• Shape based descriptor.
Ashok Veeraraghavan, Lena Gorelick, 2007
Devise a scheme for 2007
recognizing simple human
activities for assisted living
and write a journal paper.
Walk Run Faint
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10. Year 3 (Oct, 2012 – Sep, 2013)
Evaluate the performance with
end users.
• Real time setup in an
eldercare home.
•Verification of results.
•Refinement of the algorithms.
Thesis writing and PhD Viva.
9
Sven Fleck, 2008
11. Progress to date
Original Video Silhouette Extraction
by Optical flow
Motion Vectors Bounding Box
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Editor's Notes
Frame differencing.Optical flow.
We regard human actions as three dimensional shapes induced by the silhouettes in the space-time volume.Represent an action in a video sequence by a 3D point cloud extracted by sampling 2D silhouettes over time
Tracking is further divided into motion, shape, appearance and depth data with respect to the type of image segmentation. We then derive directionality-based feature vectors (directional vectors) from the silhouette contours and use the distinct data distribution of directional vectors in a vector space for clustering and recognition.
To evaluate the system within a real-world scenario, a full prototype system has been installed within a house owned by Germany’s leading provider for assisted living homes for the elderly. The system has been running 24/7 for several months now since its installation in early 2007 (with no activity recognition at that time). The algorithms have been developed with this system as testbed.
