In this paper, we propose a robust object tracking
algorithm based on a sparse collaborative model that exploits
both holistic templates and local representations to account for
drastic appearance changes. Within the proposed collaborative
appearance model, we develop a sparse discriminative classifier
(SDC) and sparse generative model (SGM) for object tracking.
In the SDC module, we present a classifier that separates
the foreground object from the background based on holistic
templates. In the SGM module, we propose a histogram-based
method that takes the spatial information of each local patch into
consideration. The update scheme considers both the most recent
observations and original templates, thereby enabling the proposed
algorithm to deal with appearance changes effectively and
alleviate the tracking drift problem. Numerous experiments on
various challenging videos demonstrate that the proposed tracker
performs favorably against several state-of-the-art algorithms.