Object Tracking Based on Online Classification Boosted by Discriminative Features

Abstract

We propose a robust object tracking algorithm under "tracking by detection" framework.A sparse random Gaussian-distributed measurement matrix is used to build an object appearance model in the compressed domain, and detection is done by our semi-supervised learning process.Our method trains a group of weaker classifiers corresponding to every feature, and linearly combines them using the weights computed by its discriminative power to build a strong classification function, which detects a target from background.Kalman filtering is also adopted to smoothen the tracking results.Experiments show the proposed classification improves performance in tracking without drift.