Lightweight Deep Neural Network for Real-Time Visual Tracking with Mutual Learning

Haojie Zhao, Gang Yang, Dong Wang, Huchuan Lu

Abstract: In this work, we develop a real-time tracking algorithm with a lightweight deep neural network. The contributions of this work mainly include two aspects. First, we reformulate the discriminative correlation filter (DCF) based tracker as a fully convolutional neural network and design an effective end-to-end tracking framework. Second, we build our tracker with a pruned convolutional neural network, which is trained by a mutual learning approach to further improve the location accuracy. The proposed tracking algorithm can track objects at 60 FPS. Extensive experiments on OTB2013, OTB2015 and VOT2017 Real-time demonstrate that the proposed tracker performs favorably against state-of-the-art methods.