1. Real Time Video Segmentation Feng Xie

2. Motivation 4 Video compositing & layering 4 Video Avatar 4 Object Recognition 4 Video understanding 4 Video Surveillence

3. Overview Comparison 4 Sample based segmentation Simple and easy to implement and accelerate insensitive to object or scene space complexity sensitive to lighting and color changes 4 Model based segmentation more robust against light and color changes by exploiting model and motion continuity complexity increases with scene complexity

4. Real Time Human Tracking C.Wren, etc, "PFinder:Real-Time Tracking of the Human Body", PAMI, 1997, pp 780-785C.Wren"PFinder:Real-Time Tracking of the Human Body" Segmentation using Mahalanobis distance

5. Adaptive background elimination A. Francois and G. Medioni on Adaptive Color Background Modeling for Real-Time Segmentation of Video StreamsA. FrancoisG. MedioniAdaptive Color Background Modeling for Real-Time Segmentation of Video Streams

6. Video Avatar for Lecture of the Future 4 Real time motion can be sudden and jerky 4 Lots of lighting changes due to highly emmisive and specular surfaces 4 Shadows, reflections and occlusion

7. Prototype Implementation 4 Color Differencing 4 Hue Differencing (for removing shadows) 4 Mophological Filtering removing noisy outliers and patch up holes 4 Connected Component removing big pieces of outliers.

8. Results 4 System works well for room when lighting condition is fairly stable the foreground human is well lit most of parts of the human is well contrasted with the background room.

9. Results 4 System fails when lighting condition is highly variant foregound human is dark major part of the human has the same color as the background.

10. Video Demos 4 Feng Feng 4 Segmented Feng Segmented Feng 4 James James 4 Segmented James Segmented James 4 Milton Milton 4 Segmented Milton SegmentedMilton