Boosting Correlation Filter Based Tracking Using Multi Convolutional Features

Abstract

Correlation filter based tracking algorithms have been commonly used in object tracking community. Recently, hand-craft features are replaced by deep convolutional features pre-trained on lager scale image datasets. The low level features with high resolution can locate the position of targets more accurate while the high level features contain more semantic information. In this paper, we construct several single conv-feature correlation filters as weak classifiers. Then, we apply boosting learning method to train a multi conv-features tracker for combining both high resolution features and semantic features. The boosting learner assigns adaptive weights for weak classifiers and the position of target is estimated by the adaptive weighted response map. Experimental results on comprehensive dataset OTB2013 demonstrate that our tracking algorithm can achieve accurate and robust performance compared with baselines and other state-of-art trackers.