A benchmark for robustness analysis of visual tracking algorithms

Abstract

In this study, we investigate the robustness of existing visual tracking algorithms with quality-degraded video. A video database including the reference video sequences and their distorted versions is created as the benchmark for robustness analysis of visual tracking algorithms. Ten existing visual tracking algorithms are used to conduct the experiments for robustness analysis based on the benchmark. Our initial investigation demonstrates that all the existing visual tracking algorithms cannot obtain the robust visual tracking results for quality-degraded video sequences. The experimental results in this study show that there is still much room for the design of robust visual tracking algorithms.