26 research outputs found
Improving Model Drift for Robust Object Tracking
Discriminative correlation filters show excellent performance in object
tracking. However, in complex scenes, the apparent characteristics of the
tracked target are variable, which makes it easy to pollute the model and cause
the model drift. In this paper, considering that the secondary peak has a
greater impact on the model update, we propose a method for detecting the
primary and secondary peaks of the response map. Secondly, a novel confidence
function which uses the adaptive update discriminant mechanism is proposed,
which yield good robustness. Thirdly, we propose a robust tracker with
correlation filters, which uses hand-crafted features and can improve model
drift in complex scenes. Finally, in order to cope with the current trackers'
multi-feature response merge, we propose a simple exponential adaptive merge
approach. Extensive experiments are performed on OTB2013, OTB100 and TC128
datasets. Our approach performs superiorly against several state-of-the-art
trackers while runs at speed in real time.Comment: 7 pages, 6 figures, 4 table