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Kernel Based Multiple Cue Adaptive Appearance Model For Robust Real-time Visual Tracking
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DOI：10.1109/LSP.2013.2278400.png)
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Abstract
En 中文
In this letter, we propose a robust and real-time visual tracking algorithm via a novel kernel based multiple cue adaptive appearance model (KBMCAAM). In particular, the appearance model is constructed with a naive Bayes classifier which is trained utilizing sparse multi-scale Haar-like features weighted by a spatial kernel function. Moreover, multiple image cues are integrated to improve the model's discriminative capacity. Experimental results demonstrate the superior performance of our proposed method to many state-of-art algorithms.
Keywords:
Adaptive appearance model
kernel function
multiple image cues
real-time object tracking
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