Object occlusion in the context of computer vision refers to the event where a part or all of an object in the scene is hidden from view by some other object in the scene. In simple words, when an object is in front of another object, blocking it from view, we say that the second object is occluded.
For example, imagine you are looking at a photograph of a crowd of people. Some of those people may be standing in front of others, preventing you from seeing the people behind them completely. The people behind are occluded by the people in front.
Occlusion poses a significant challenge in many areas of computer vision, such as object recognition, tracking, and 3D reconstruction. For instance, if a rescue robot is using computer vision to identify people in need of help, it might struggle if those people are partially or completely hidden by debris or other obstacles.
To handle occlusion, various strategies can be used. Some techniques involve using predictive models to guess what's behind the occlusion based on the visible parts of the object. Others might involve integrating data from multiple cameras or sensors to get different views of the scene, or using sensors like LiDAR that can sometimes see through occlusions (for example, if the occluding object is semi-transparent for the LiDAR's laser wavelengths).