Object Tracking Using Minimum Spanning Tree One Class Classifier
In the traditional discriminative object tracking methods, similar to the target, background is often taken mistakenly as a single negative class to train two class classifiers despite including various objects with different distributions. A novel method based on one class classifier is proposed for object tracking. It takes background as a collection of all other classes except the target. Object tracking is viewed as a one class classification problem correspondingly. Firstly, the tracking results in the recent few frames are collected as the target training samples. Secondly, a k nearest graph is built based on these training samples and the most representative samples are selected on the basis of their own local average degrees to construct a minimum spanning tree. Then an one class classifier based on the minimum spanning tree is trained to model the target. Lastly the similarity scores of candidates drawn from a particle filter are evaluated by the trained one class classifier and the one with highest score is taken as the tracking result in current frame and used to update the training sample set and the one class classifier. Empirical results on challenging video sequences demonstrate the superior performance of our method in robustness and accuracy to state of the art methods.
2016 9th International Symposium on Computational Intelligence and Design (ISCID)