title: Optimal Appearance Model for Visual Tracking.
abstract: Many studies argue that integrating multiple cues in an adaptive way increases tracking performance. However, what is the definition of adaptiveness and how to realize it remains an open issue. On the premise that the model with optimal discriminative ability is also optimal for tracking the target, this work realizes adaptiveness and robustness through the optimization of multi-cue integration models. Specifically, based on prior knowledge and current observation, a set of discrete samples are generated to approximate the foreground and background distribution. With the goal of optimizing the classification margin, an objective function is defined, and the appearance model is optimized by introducing optimization algorithms. The proposed optimized appearance model framework is embedded into a particle filter for a field test, and it is demonstrated to be robust against various kinds of complex tracking conditions. This model is general and can be easily extended to other parameterized multi-cue models.
authors.affiliation: Computer Science and Information Technology, North-East Normal University, Changchun, Jilin Province, China.
authors.forename: Yuru
authors.initials: Y
authors.lastname: Wang
authors.affiliation: School of Information Engineering, Jilin Business and Technology College, Changchun, Jilin Province, China.
authors.forename: Longkui
authors.initials: L
authors.lastname: Jiang
authors.affiliation: Computer Science and Information Technology, North-East Normal University, Changchun, Jilin Province, China.
authors.forename: Qiaoyuan
authors.initials: Q
authors.lastname: Liu
authors.affiliation: Computer Science and Information Technology, North-East Normal University, Changchun, Jilin Province, China.
authors.forename: Minghao
authors.initials: M
authors.lastname: Yin
mesh_terms: D006801:Humans; D008959:Models, Neurological; D009039:Motion Perception / Q000502:physiology*