Co-Tracking Using Semi-Supervised Support Vector Machines
Co-Tracking Using Semi-Supervised Support Vector Machines
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DOI：
10.1109/iccv.2007.4408954
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发表时间：
2007-12
期刊：
影响因子：
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通讯作者：
Feng Tang;Shane Brennan;Qi Zhao;Hai Tao
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文献类型：
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作者：
Feng Tang;Shane Brennan;Qi Zhao;Hai Tao
本文将跟踪视为前景/背景分类问题，并提出了一种在线半监督学习框架。半监督学习以少量标记样本初始化，将每个新样本视为未标记数据。新数据的分类和分类器的更新在协同训练框架中同时实现。该对象使用独立的特征来表示，并为每个特征构建一个在线支持向量机（SVM）。通过使用分类器加权方法组合来自每个分类器的置信度图来融合来自不同特征的预测，该方法创建比基于单个特征的任何分类器表现更好的最终分类器。然后，半监督学习方法使用组合置信图的输出来生成新样本并在线更新 SVM。通过这种方法，跟踪器可以获得越来越多的关于物体和背景的知识，并随着时间的推移不断改进自身。与其他判别式跟踪器相比，在线半监督学习方法使用来自其他特征的信息改进了每个单独的分类器，从而形成更强大的跟踪器。实验表明，该框架在具有挑战性的序列上比最先进的跟踪算法表现更好。
This paper treats tracking as a foreground/background classification problem and proposes an online semi- supervised learning framework. Initialized with a small number of labeled samples, semi-supervised learning treats each new sample as unlabeled data. Classification of new data and updating of the classifier are achieved simultaneously in a co-training framework. The object is represented using independent features and an online support vector machine (SVM) is built for each feature. The predictions from different features are fused by combining the confidence map from each classifier using a classifier weighting method which creates a final classifier that performs better than any classifier based on a single feature. The semi-supervised learning approach then uses the output of the combined confidence map to generate new samples and update the SVMs online. With this approach, the tracker gains increasing knowledge of the object and background and continually improves itself over time. Compared to other discriminative trackers, the online semi-supervised learning approach improves each individual classifier using the information from other features, thus leading to a more robust tracker. Experiments show that this framework performs better than state-of-the-art tracking algorithms on challenging sequences.