Recently, combining multiple features into discriminative correlation filters to improve
tracking representation has shown great potential in object tracking. Existing trackers apply …

For visual tracking methods based on kernel support vector machines (SVMs), data
sampling is usually adopted to reduce the computational cost in training. In addition …

Discriminative correlation filters (DCFs) have been widely used in the tracking community
recently. DCFs-based trackers utilize samples generated by circularly shifting from an image …

Correlation filters (CFs) have been extensively used in tracking tasks due to their high
efficiency although most of them regard the tracked target as a whole and are minimally …

Discriminative Correlation Filter (DCF) based trackers are quite efficient in tracking objects
by exploiting the circulant structure. The kernel trick further improves the performance of …

In recent years, the ensembled trackers composed of multi-level features from the pre-
trained Convolutional Neural Network (CNN) have achieved top performance in visual …

Common tracking algorithms only use a single feature to describe the target appearance,
which makes the appearance model easily disturbed by noise. Furthermore, the tracking …

In this work, we propose a novel adaptive spatially-regularized correlation filters (ASRCF)
model to simultaneously optimize the filter coefficients and the spatial regularization weight …

During the recent years, correlation filters have shown dominant and spectacular results for
visual object tracking. The types of the features that are employed in this family of trackers …

Over these years, Correlation Filter-based Trackers (CFTs) have aroused increasing
interests in the field of visual object tracking, and have achieved extremely compelling …