Robust Visual Tracking Using Dynamic Classifier Selection with Sparse Representation of Label Noise
This work addresses the drifting issue in visual tracking for applications like surveillance and robotics, but it is incremental as it builds on existing tracking-by-detection and part-based online boosting frameworks.
The paper tackled the drifting problem in visual tracking caused by incorrect labels in online classifier updates by modeling label noise with sparse representation and proposing a dynamic classifier selection method. The result was a robust tracker that outperformed state-of-the-art methods on 12 challenging sequences with severe occlusions, illumination changes, and pose variations.
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.