Robust L1 tracker with CNN features

Abstract

Recently, L1 tracker has been widely applied and received great success in visual tracking. However, most L1 trackers use only the image intensity for sparse representation, which is insufficient to represent the object especially when drastic appearance changes occur. Convolutional neural network (CNN) has demonstrated remarkable capability in a wide range of computer vision fields, and features extracted from different convolutional layers have different characteristics. In this paper, we propose a novel sparse representation model with convolutional features for visual tracking. Besides, to alleviate the redundancy from high-dimensional convolutional features, a feature selection method is adopted to remove noisy and irrelevant feature maps, which can reduce computation redundancy and improve tracking accuracy. Different from traditional sparse representation based tracking methods, our model not only exploits convolutional features to improve the robustness for describing the object appearance but also uses the trivial templates to model both reconstruction errors caused by sparse representation and the eigensubspace representation. In addition, an unified objective function is proposed and a customized APG method is developed to effectively solve the optimization problem. Numerous qualitative and quantitative evaluations demonstrate that our tracker outperforms other state-of-the-art trackers in a wide range of tracking scenarios.