Current video tracking systems often employ a rich set of intensity, edge, texture, shape and object level features combined with descriptors for appearance modeling. This approach increases tracker robustness but is compu- tationally expensive for realtime applications and localization accuracy can be adversely affected by including distracting features in the feature fusion or object classification processes. This paper explores offline feature subset selection using a filter-based evaluation approach for video tracking to reduce the dimensionality of the feature space and to discover relevant representative lower dimensional subspaces for online tracking. We com- pare the performance of the exhaustive FOCUS algorithm to the sequential heuristic SFFS, SFS and RELIEF feature selection methods. Experiments show that using offline feature selection reduces computational complex- ity, improves feature fusion and is expected to translate into better online tracking performance. Overall SFFS and SFS perform very well, close to the optimum determined by FOCUS, but RELIEF does not work as well for feature selection in the context of appearance-based object tracking.