Tracking Using On-line Feature Selection and a Local Generative ModelAbstract
This paper proposes an algorithm for online feature selection which improves robustness to occlusions by referring to a localized generative appearance model. Discriminative classifiers based on feature extraction have classically either prepared a fixed prior model by training offline, or continually adapted their classification parameters to any apparent appearance changes. By combining the attractive qualities of each approach, our framework can cope with appearance changes of a target object and will maintain proximity to a static appearance model. Our main contribution is the use of a generative model to guide the online feature selection to regions of an image which maintain a valid appearance. The generative model exhibits the properties of non-negativity, localization and orthogonality. We demonstrate the system in a tracking framework
to show improved tracking performance through occlusions.
- Being able to localize occlusions in an image is useful technique for preventing drift in an on-line learning tracker.
- However, the appearance model takes away too much of the flexibility afforded by the on-line learning approach; a model which admits more flexibility in appearance (e.g pose changes) would better complement the tracker.