Robust Visual Vocabulary Tracking Using Hierarchical Model Fusion

In this paper, we propose a new visual tracking approach based on the Hierarchical Model Fusion framework, which fuses two different trackers to cope with different tracking problems. We use an Incremental Multiple Principal Component Analysis tracker as our main model as well as an image patch tracker as our auxiliary model. Firstly, we randomly sample image patches within the target region obtained by the main model in the training frames for constructing a visual vocabulary using Histogram of Oriented Gradient features. Secondly, we use a supervised learning algorithm based on a Gaussian Mixture Model, which not only operates on supervised information to improve the discriminative power of the clusters, but also increases the purity of the clusters. Then, auxiliary models are initialised by obtaining confidence scores of image patches based on the similarity between candidates and codewords. In addition, an updating procedure and a result refinement scheme are included in the proposed tracking approach. Experiments on challenging video sequences demonstrate the robustness of the proposed approach to handling occlusion, pose variation and rotation.