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

Background Removal Process

Background Removal Process – An Example

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

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)‏

Sample results and conclusions

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

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