Computer Vision Home Assignment Module #3 UCU
Folder matching contains notebook with template matching examples SAD, SSD, NCC
File tracker.py contains the first version of Lukas-Kanade tracker implementation and it can be launched from the console with a command like:
python tracker.py "./Toy/img/" 152 102 192 169 -debug 20where 152 102 192 169 are roi coordinates x1 y1 x2 y2, -debug is for IPython mode so the frames can be plotted and 20 is the number of frames. The rest of the scripts are made in IPython version only. The script can draw frames in IPython and writes a video file.
Tracker performance is not good. For example, in Toy dataset the roi degenerates when non-affine transformations occur
Partially it can be explained by cv.warpAffine restrictions - this operation has a reference point at the top left corner. So when the roi is rotated, the algorithm can't follow due to the long lever (it can't rotate around the center of roi - these dependencies are not reflected in the derivative Jacobi matrix).
I tried to solve it by clipping the image to make this reference point closer to the roi and restricting scaling position in the Jacobi matrix but it didn't help much.
In the second version I implemented custom affine transformation with a reference point at the center of the roi. But it appears slow and has some bugs (like scaling overshooting, so I had to correct by restricting scaling in warp matrix) Although it performs slightly better with roi rotations but eventually, the degeneration of roi is inevitable as well.
In Car sequence the roi degenerates due to extra information on background so it expands to track the moving background.
Pyramidal extention performs worse - I can explain by inacuracies of roi during the transition between pyramidal levels.
The results are in the folder outputs: video and html files.
So at this point, I have to give up, because the task already took quite a long time to tune the algorithm.
Folder drafts contains draft notebook from Colab