PROST: Parallel robust online simple tracking

Jakob Santner, Christian Leistner, Amir Saffari, Thomas Pock, Horst Bischof
Institute for Computer Graphics and Vision, Graz University of Technology, Graz, Austria
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010


   title={PROST: Parallel robust online simple tracking},

   author={Santner, J. and Leistner, C. and Saffari, A. and Pock, T. and Bischof, H.},

   booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010},




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Tracking-by-detection is increasingly popular in order to tackle the visual tracking problem. Existing adaptive methods suffer from the drifting problem, since they rely on self-updates of an on-line learning method. In contrast to previous work that tackled this problem by employing semi-supervised or multiple-instance learning, we show that augmenting an on-line learning method with complementary tracking approaches can lead to more stable results. In particular, we use a simple template model as a non-adaptive and thus stable component, a novel optical-flow-based mean-shift tracker as highly adaptive element and an on-line random forest as moderately adaptive appearance-based learner. We combine these three trackers in a cascade. All of our components run on GPUs or similar multi-core systems, which allows for real-time performance. We show the superiority of our system over current state-of-the-art tracking methods in several experiments on publicly available data.
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