Deep scale feature for visual tracking

Abstract

Recently, deep learning methods have been introduced to the field of visual tracking and gain promising results due to the property of complicated feature. However existing deep learning trackers use pre-trained convolution layers which is discriminative to specific object. Such layers would easily make trackers over-fitted and insensitive to object deformation, which makes tracker a good locator but not a good scale estimator. In this paper, we propose deep scale feature and an algorithm for robust visual tracking. In our method, object scale estimator is made from lower layers from deep neural network and we add a specially trained mask after convolution layers, which filters out potential noise in this tracking sequence. Then, the scale estimator is integrated into a tracking framework combined with locator made from powerful deep learning classifier. Furthermore, inspired by correlation filter trackers, we propose an online update algorithm to make our tracker consistent with changing object in tracking video. Experimental results on various public challenging tracking sequences show that our proposed framework is effective and produce state-of-art tracking performance.