Introducing Visual Object Tracking From Classical Views to Machine Learning, Roman Pflugfelder
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Visual object tracking is a fundamental and important task in computer vision. Tracking is an essential prerequisite of motion analysis which is important to many problems such as motion capturing, object recognition and scene understanding. Tracking research started in the early 1950s with Claude Shannon?s exceptional work on information theory and Philip Woodward?s contributions to radar research. Since then, tracking has become a research field in various scientific disciplines, considering beside visual data, state estimation in dynamical systems or the analysis of time series. Despite the efforts, visual tracking is an open problem, lacking in a sufficient theoretical understanding and in practical algorithms for a large number of applications. This lecture will give an introduction to tracking and motion analysis, its challenges and applications supported by practical examples. We will learn the theoretical views on the problem, which are currently prevalent in literature, especially by focusing on tracking single objects. Finally, the talk will present a rather popular view on tracking by seeing the problem from a machine learning perspective. A compact overview of different learning situations such as adaptation, semi-supervised learning, unsupervised deep learning with different representational models under different algorithmic design concepts is given. The talk concludes with a summary of the current state-of-the-art concerning performance, open problems and potential future work.