1 July 2006 Learning-based appearance model for probabilistic visual tracking
Author Affiliations +
Optical Engineering, 45(7), 077204 (2006). doi:10.1117/1.2227276
Abstract
In visual tracking, the object's appearance may change over time due to illumination changes, pose variations, and partial or full occlusions. This variability makes tracking difficult. This paper proposes an adaptive appearance model for visual tracking. The model can adapt to changes in object appearance over time. The value of each pixel is modeled by a Gaussian mixture distribution. A novel update scheme based on the expectation maximization algorithm is developed to update the appearance model parameters. In designing the tracking algorithm, the observation model is based on the adaptive appearance model, and a particle filter is employed. Outlier pixels and occlusions are handled using a robust-statistics technique. Numerous experimental results demonstrate that the proposed algorithm can track objects well under illumination changes, large pose variations, and partial or full occlusions.
Anping Li, Zhongliang Jing, Shi Qiang Hu, "Learning-based appearance model for probabilistic visual tracking," Optical Engineering 45(7), 077204 (1 July 2006). http://dx.doi.org/10.1117/1.2227276
JOURNAL ARTICLE
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KEYWORDS
Detection and tracking algorithms

Expectation maximization algorithms

Visual process modeling

Motion models

Particle filters

Optical tracking

Algorithm development

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