Robust Tracking Using On-Line Selection of Multiple Features
Sihua Yi, Zhijun Yao, Juntao Liu, Jun Chen, Wenyu Liu · 2012
This paper presents a novel on-line feature selection mechanism based on mean shift tracking algorithm, which adjusts the weight for each feature and each bin in feature histograms during the tracking process, according to the discrimination between the appearance of object and background with different features. Then the tracking performance would be stable and reliable using those features combined by the weights. As the appearance model, we select features of gray level, Local Binary Patterns (LBP) texture and edge orientation. The gradient amplitude is supplement to the feature space for tracking. Experiments on two video sequences show the effectiveness of the proposed method.