title: Visual tracker using sequential bayesian learning: discriminative, generative, and hybrid.
abstract: This paper presents a novel solution to track a visual object under changes in illumination, viewpoint, pose, scale, and occlusion. Under the framework of sequential Bayesian learning, we first develop a discriminative model-based tracker with a fast relevance vector machine algorithm, and then, a generative model-based tracker with a novel sequential Gaussian mixture model algorithm. Finally, we present a three-level hierarchy to investigate different schemes to combine the discriminative and generative models for tracking. The presented hierarchical model combination contains the learner combination (at level one), classifier combination (at level two), and decision combination (at level three). The experimental results with quantitative comparisons performed on many realistic video sequences show that the proposed adaptive combination of discriminative and generative models achieves the best overall performance. Qualitative comparison with some state-of-the-art methods demonstrates the effectiveness and efficiency of our method in handling various challenges during tracking.
authors.affiliation: Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
authors.forename: Yun
authors.initials: Y
authors.lastname: Lei
authors.forename: Xiaoqing
authors.initials: X
authors.lastname: Ding
authors.forename: Shengjin
authors.initials: S
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D016002:Discriminant Analysis; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity