Can anyone please explain how segmentaion during detection helps tracking of an object?

As SiamMask is a Tracking By Segmentation pipeline and this helps to keep a more accurate representation of the object across time.
Here in the codes can someone please point out where is segmentation results are being integrated during tracking?

@SamihaSara
From tools/test.py, you can see codes like:

 if refine_enable:
            mask = net.track_refine((delta_y, delta_x)).to(device).sigmoid().squeeze().view(
                p.out_size, p.out_size).cpu().data.numpy()

in this codes, mask is the segmentation result obtained from the refinement module.
The final rotated bbox is computed from this mask.

@zosel260 If I want to use MinMax bounding Box by un-commenting the following line-->

SiamMask/tools/test.py

Line 293 in 0eaac33

# pbox = cv2.boundingRect(polygon) # Min Max Rectangle

I get the following error:
location = state2[obj_id]['ploygon'].flatten()
AttributeError: 'tuple' object has no attribute 'flatten'

Can you please suggest how to solve this issue?

@zosel260 If I want to use MinMax bounding Box by un-commenting the following line-->

SiamMask/tools/test.py

Line 293 in 0eaac33

# pbox = cv2.boundingRect(polygon) # Min Max Rectangle

I get the following error:
location = state2[obj_id]['ploygon'].flatten()
AttributeError: 'tuple' object has no attribute 'flatten'

Can you please suggest how to solve this issue?

could you solve this problem??

@ahmedosamaz No, I have not. Still waiting for suggestions from others.