1. David Harwin Adviser: Petros Faloutsos
Background Removal
David Harwin
Adviser: Petros Faloutsos

2. Background Removal Process

3. Background Removal Process – An Example

4. Side Note
In this implementation, the segmenter is set to produce binarized masks corresponding to per- pixel FG/BG segmentation.
Interestingly enough, the non-binarized grayscale difference has potential in background completion

5. Measuring Accuracy
Raw accuracy (% correct pixels) show how well the removal was performed, but are dependent on the number of foreground pixels
Since this is essentially a classification problem, it is reasonable to define optimal behavior as minimizing both the false accept rate (FAR) and false reject rate (FRR)‏

6. Sample results and conclusions

7. Challenges and Ideas
A significant number of pixels either washed out or faded to black, making color differencing problematic
this suggests that black/white pixels should be treated as a special case
no success getting the framework to import video files, however
same method- frames read as still images
video formats not suitible for verification against ground truth data

8. The next steps current masks have rough edges and holes
filling algorithms largely domain-specific
smoothing – create averaged map at 1:2^x scale
try edge-finding algorithm and filling techniques
multiframe implementation – supplement BG model with motion likelihood updated each frame