EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
International Journal of Computer Vision
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online Selection of Discriminative Tracking Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Sequential Kernel Density Approximation and Its Application to Real-Time Visual Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust online appearance models for visual tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper proposes a new tracking algorithm which combines object and background information, via building object and background appearance models simultaneously by nonparametric kernel density estimation. The major contribution is a novel bidirectional learning framework for discrimination between the object and background. It has the following advantages: 1) it embeds background information, unlike most other methods that focus on the object only, 2) it provides a mechanism to detect occlusion and distraction, which are two main causes of tracking failure, 3) it performs feature selection, making the tracker more robust to outliers. By this learning framework, we are able to embed discriminative information into the generative appearance model. Experimental results demonstrate that the tracker is able to model drastic appearance changes and robust to occlusion and distraction.