Detecting Movement cont.

We tried to use blob detection in order to approximate the movement of the mouse. We took the centroid of the blobs to represent the center mass of the mouse, and the change in position of the centroid would approximate the change in the mouse position. However, using blobs this way turned out to be a chaos, we detected more noise than the actual movement of the mouse.

To resolve this issue, we tried using the cuboids. We computed a binary image of the cuboids that appear, and then calculate a centroid from the image. The centroid serves the same purpose as before: to approximate the center mass of the mouse. Here is the unfiltered graph of the mouse movement:

Unfiltered x and y displacement for exploring

Unfiltered x and y displacement for grooming

We noted that there is a distinctive pattern between exploring and grooming. The x and y displacement while the mouse is exploring is greater than when the mouse is grooming. Furthermore, the x and y displacement when the mouse is grooming tend to be around zero. This is intuitive because the mouse does not move as much when it is grooming than when it is exploring. We want to add this result as a feature to Piotr's cuboids, but we need to filter out more noise. We used median and average filtering to obtain the following results:

Filtered x displacement for exploring

Filtered y displacement for exploring

Filtered x displacement for grooming

Filtered y displacement for grooming

Next step:
- Create displacement graphs for all training set
- Scale the graphs to 100 frames for consistency