Background Removal

1. David Harwin Adviser: Petros Faloutsos Background Removal

2. The State of the Art • This field has been one of great interest in the past decade • There have been many recent papers on the topic with intended applications ranging from visual effects processing to ... • In order to determine how to proceed, I have spent the quarter reading and analyzing existing papers.

3. Identifying motion • pixel differencing vs optical flow • differencing vulnerable to signal noise, illumination changes, and BG motion • optical flow is computationally expensive • most opted for hybrid approach • only Criminisi (2006) avoids optical flow altogether, and instead uses spatio-temporal continuity of labels

4. Segmentation • 2-pass approach • low-level then high-level (Calderara, Tsai, Ćalić)‏ • Calderara - 2nd pass checks for consistency of changes in color and intensity over time. Objects with insufficiently coherent motion are ignored as BG • Tsai – 1st pass initial labelling (constantly vs never changing), 2nd only works on uncertain pixels • Ćalić - creates low-resolution representation, corrects BG for camera motion through matrix transformations, determines most ”salient” frames, then calculation of regions of interest occurs only on these keyframes • high-level then low-level (Park)‏ • Park - object tracking, followed by pixel differencing inside the object window • Single pass • Criminisi -

5. Key Features