<p>Additionally to what has been said I want to add the following:</p>

<ul>
<li>For Object Tracking, an essential part in dealing with occlusions is writing an efficient cost function, which will be able to discriminate between the occluded object and the object that is occluding it. If the cost function is not ok, the object instances (ids) may swap and the object will be incorrectly tracked. There are numerous ways in which cost functions can be written some methods use CNNs<a href="https://arxiv.org/pdf/1703.07402.pdf" rel="noreferrer">[1]</a> while some prefer to have more control and aggregate features<a href="https://www.mdpi.com/1424-8220/20/4/1110" rel="noreferrer">[2]</a>. The disadvantage of CNN models is that in case you are tracking objects that are in the training set in the presence of objects which are not in the training set, and the first ones get occluded, the tracker can latch onto the wrong object and may or may never recover. Here is a <a href="https://youtu.be/MMQzQW1Y4h0?t=94" rel="noreferrer">video</a> showing this. The disadvantage of aggregate features is that you have to manually engineer the cost function, and this can take time and sometimes knowledge of advanced mathematics.</li>
<li><p>In the case of dense Stereo Vision reconstruction, occlusion happens when a region is seen with the left camera and not seen with the right(or vice versa). In the disparity map this occluded region appears black (because the corresponding pixels in that region have no equivalent in the other image). Some techniques use the so called background filling algorithms which fill the occluded black region with pixels coming from the background. Other reconstruction methods simply let those pixels with no values in the disparity map, because the pixels coming from the background filling method may be incorrect in those regions. Bellow you have the 3D projected points obtained using a dense stereo method. The points were rotated a bit to the right(in the 3D space). In the presented scenario the values in the disparity map which are occluded are left unreconstructed (with black) and due to this reason in the 3D image we see that black "shadow" behind the person.</p>

<p><a href="https://i.stack.imgur.com/oVoKk.png" rel="noreferrer"><img src="https://i.stack.imgur.com/oVoKk.png" alt="enter image description here"></a></p></li>
</ul>
Image Processing: What are occlusions?
<p>I'm developing an image processing project and I come across the word <strong><em>occlusion</em></strong> in many scientific papers, what do occlusions mean in the context of image processing? The dictionary is only giving a general definition. Can anyone describe them using an image as a context?</p>