Co-Tracking Using Semi-Supervised Support Vector Machines

This paper treats tracking as a foreground/background classification problem and proposes an online semisupervised 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 its...

• Type: Conference
• Year: 2007
• Where: ICCV
• Authors: Feng Tang, Shane Brennan, Qi Zhao, Hai Tao
• Keywords: Computer Vision, ICCV 2007, Semi-supervised Learning, Semi-supervised Learning Approach, Semi-supervised Learning Treats