1 research outputs found
Robust Visual Tracking Using Dynamic Classifier Selection with Sparse Representation of Label Noise
Recently a category of tracking methods based on "tracking-by-detection" is
widely used in visual tracking problem. Most of these methods update the
classifier online using the samples generated by the tracker to handle the
appearance changes. However, the self-updating scheme makes these methods
suffer from drifting problem because of the incorrect labels of weak
classifiers in training samples. In this paper, we split the class labels into
true labels and noise labels and model them by sparse representation. A novel
dynamic classifier selection method, robust to noisy training data, is
proposed. Moreover, we apply the proposed classifier selection algorithm to
visual tracking by integrating a part based online boosting framework. We have
evaluated our proposed method on 12 challenging sequences involving severe
occlusions, significant illumination changes and large pose variations. Both
the qualitative and quantitative evaluations demonstrate that our approach
tracks objects accurately and robustly and outperforms state-of-the-art
trackers.Comment: accepted at ACCV2012, Ora