Visual tracking is one of the most classical problems in computer vision, and its history goes back to the beginning of the 80s when classical concepts such as the Lucas-Kanade tracker and matched filters were developed. The purpose of this chapter is to give an overview of the development of the field, starting from those two approaches and concluding with deep learning-based approaches as well as the transition to video segmentation. The overview is limited to holistic models for generic tracking in the image plane, and a particular focus is given to discriminative models, the MOSSE (minimum output sum of squared errors) tracker, and DCFs (discriminative correlation filters).