The recent advent of video-capture technology and processing capabilities of small- and large-scale computers, has opened up a wealth of possibilities for exciting new applications. These range from real-time visual processing of images taken by mobile phones, autonomous surveillance systems, human-computer interfaces and autonomous cars, to off-line processing of videos taken of basketball games for automatic creation of game statistics. The new applications introduced new challenges for how to efficiently process the visual information and how to interpret it automatically. The Advanced Topics in Computer Vision course will cover selected solutions that have been proposed over the recent years to solve some of these problems with focus on motion computation and object tracking. The course will cover optical flow computation techniques, different tracking techniques, visual models, dynamic models, Bayes recursive filters -- including the Kalman filter as well as their Monte Carlo counterparts, the particle filters -- and learn how to design and validate a state-of-the-art tracker. We will review the results of the latest tracking challenges like VOT2013 and VOT2014. Students that successfully finish this course will understand the main tracking techniques and will be able to implement working trackers for tracking arbitrary objects in video sequences.