  This paper presents a novel approach to visual tracking: Similarity Matching
Ratio (SMR). The traditional approach of tracking is minimizing some measures
of the difference between the template and a patch from the frame. This
approach is vulnerable to outliers and drastic appearance changes and an
extensive study is focusing on making the approach more tolerant to them.
However, this often results in longer, corrective algo- rithms which do not
solve the original problem. This paper proposes a novel approach to the
definition of the tracking problems, SMR, which turns the differences into a
probability measure. Only pixel differences below a threshold count towards
deciding the match, the rest are ignored. This approach makes the SMR tracker
robust to outliers and points that dramaticaly change appearance. The SMR
tracker is tested on challenging video sequences and achieved state-of-the-art
performance.
