Background Removal

1. David Harwin Adviser: Petros Faloutsos Background Removal

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