Abstract
This paper presents a learning-based algorithm for object tracking. During on-line learning we employ most informative and hard to classify examples, features maximizing individually the mutual information, stable object features within all past observations and features from the initial object template. The object undergoing tracking is discriminated by a boosted classifier built on regression stumps. We seek mode in the confidence map calculated by the strong classifier to sample new features. In a supplementing tracker based upon a particle filter we use a recursively updated mixture appearance model, which depicts stable structures in images seen so far, initial object appearance as well as two-frame variations. The update of slowly varying component is done using only pixels that are classified by the strong classifier as belonging to foreground. The estimates calculated by particle filter allow us to sample supplementary features for learning of the classifier. The performance of the algorithm is demonstrated on freely available test sequences. The resulting algorithm runs in real-time.
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Kwolek, B. (2007). Learning-Based Object Tracking Using Boosted Features and Appearance-Adaptive Models. In: Blanc-Talon, J., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2007. Lecture Notes in Computer Science, vol 4678. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74607-2_13
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DOI: https://doi.org/10.1007/978-3-540-74607-2_13
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