Visual tracking: from correlation filters to end-to-end discriminative model prediction

Martin Danelljan (ETH Zurich, Switzerland)


Visual tracking is one of the fundamental problems in computer vision. In its most general form, no prior knowledge about the target object is given, except for its initial location. The unconstrained nature of this problem makes it particularly difficult, yet applicable to a wider range of scenarios. The same holds for the related problem of Video Object Segmentation, where the task is to predict a pixel-wise segmentation mask of the target. Due to the lack of a-priori knowledge in these problems, the method must learn an appearance model of the target online. Cast as a machine learning problem, it imposes several major challenges. Many of these challenges have been successfully addressed by developing powerful and efficient discriminative models of the target appearance.

This talk will give an overview of such approaches, starting with the widely popular Discriminative Correlation Filter (DCF) framework. The talk will focus on the most recent developments in the field, which includes deep architectures capable of directly predicting a discriminative target appearance model, that itself can be trained end-to-end in a meta-learning fashion


Martin Danelljan is a postdoctoral researcher at ETH Zurich, Switzerland. He received his Ph.D. degree from Linköping University, Sweden in 2018. His Ph.D. thesis was awarded the biannual Best Nordic Thesis Prize at SCIA 2019. His main research interests are online and meta-learning methods for visual tracking and video object segmentation, deep probabilistic models for image generation, and machine learning with no or limited supervision. His research in the field of visual tracking, in particular, has attracted much attention. In 2014, he won the Visual Object Tracking (VOT) Challenge and the OpenCV State-ofthe-Art Vision Challenge. Furthermore, he achieved top ranks in VOT2016 and VOT2017 challenges. He received the best paper award at ICPR 2016 and best student paper at BMVC 2019.