A Novel Tracking Method Based on Ensemble Metric Learning

Abstract

In recent years, object tracking is often formulated as detection tasks. Although many methods of machine learning and statistical learning can effectively achieve discriminate target model, but they usually require a large amount of training samples for satisfactory precision. Combining advantages of ensemble learning and the support vector machine, this paper proposes a simple tracking method based on metric learning. With a small number of training data sampled from the sequence of images during the tracking, we are learning an adaptive metric matrix that tends to maximum the distance between samples of different classes. In the process of tracking, metric matrix is learned and updated constantly to achieve good discrimination and adaptability. Experimental results show that our method has better tracking stability and accuracy.