Abstract
Detecting moving objects in video sequences may be particularly challenging because of the characteristics of the objects, such as their size, colour, contrast, velocity and trajectory. Industrial video tagging systems should generate tags based on information inferred from video frames and learn relations between given concepts. In opposite to traditional methods such systems should effectively segment semantic objects in tagged videos, even when the image-based object detectors provide inaccurate proposals. Such systems usually base on the knowledge which is constantly updated to acquire the dynamics of the indexed concepts. In this paper we present a short review of the most importants algorithms for detection of markers in video sequences and problems related with practical applications.
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