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|a Object tracking in video has been an active research for decades. This interest is motivated by numerous applications, such as surveillance, human-computer interaction, and sports event monitoring. Many challenges regarding tracking objects remain, this can arise due to abrupt object motion, changing appearance patterns of objects and the scene, non-rigid object structures and most significancly occlusion of tracked object (be it object-to-object or object-to-scene occlusions). Generally, occlusion in object tracking occurs under three situations: self-occlusion, inter-object occlusion by background scene structure. Self-occlusion most frequently arises while tracking articulated objects when one part of the object occludes another. Inter-object occlusion occurs when two objects being tracked occlude each other whereas occlusion by the background occurs when a structure in the background occludes the tracked objects. Typically, tracking methods handle occlusion by modelling the object motion using linear and non-linear dynamic models. The derived models will be used to continuously predicting the object location when a tracked object is occluded until the object reappears. Examples of these methods are Kalman filtering and Particle filtering trackers. Researchers have also utilised other features to resolved occlusion, for example, silhouette projections, colour histogram and optical flow. We will present some results from a previously conducted experiment when tracking single object using Kalman filter, Particle filter and Mean Shift trackers under various occlusion situations. We will also review various other occlusion handling methods that involved using multiple cameras. In a nutshell, the goal of this paper is to discuss in detail the problem of occlusion in object tracking and review the state of the art occlusion handling methods, classify them into different categories, and identify new trends. Moreover, we discuss the important issues related to occlusion handling including the use of appropriate selection of motion models, image features and use of multiple cameras.
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