  Visual object tracking is a challenging computer vision task with numerous
real-world applications. Here we propose a simple but efficient Spectral Filter
Tracking (SFT)method. To characterize rotational and translation invariance of
tracking targets, the candidate image region is models as a pixelwise grid
graph. Instead of the conventional graph matching, we convert the tracking into
a plain least square regression problem to estimate the best center coordinate
of the target. But different from the holistic regression of correlation filter
based methods, SFT can operate on localized surrounding regions of each pixel
(i.e.,vertex) by using spectral graph filters, which thus is more robust to
resist local variations and cluttered background.To bypass the eigenvalue
decomposition problem of the graph Laplacian matrix L, we parameterize spectral
graph filters as the polynomial of L by spectral graph theory, in which L k
exactly encodes a k-hop local neighborhood of each vertex. Finally, the filter
parameters (i.e., polynomial coefficients) as well as feature projecting
functions are jointly integrated into the regression model.
