The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Neural Network-Based Face Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Statistical Learning of Multi-view Face Detection
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part IV
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Object detection using image reconstruction with PCA
Image and Vision Computing
Optimal feature selection for support vector machines
Pattern Recognition
Expert Systems with Applications: An International Journal
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We update the SVM score of an object through a video sequence with a small and variable subset of support vectors. In the first frame we use all the support vectors to compute the SVM score of the object but in subsequent frames we use only a small and variable subset of support vectors to update the SVM score. In each frame we calculate the dot-products of the support vectors in the subset with the pattern of the object being tracked. The difference in the dot-products, between past and current frames, is used to update the SVM score. This is done at a fraction of the computational cost required to re-evaluate the SVM score from scratch in every frame. The two methods we develop are "Cyclic subset selection", in which we break the set of all support vectors into subsets of equal size and use them cyclically, and "Maximum variance subset selection", in which we choose the support vectors whose dot-product with the test pattern varied the most in previous frames. We combine these techniques together for the problem of maintaining the SVM score of objects through a video sequence. Results on real video sequences are shown.