Visual Tracking with Similarity Matching Ratio
This addresses robustness in visual tracking for applications like surveillance or robotics, but it appears incremental as it modifies an existing paradigm rather than introducing a new one.
The paper tackled visual tracking by proposing the Similarity Matching Ratio (SMR), which converts differences into a probability measure to ignore outliers and drastic appearance changes, achieving state-of-the-art performance on challenging video sequences.
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.