A NEW FRAMEWORK FOR MULTIPLE DEEP CORRELATION FILTERS BASED OBJECT TRACKING

In recent years, Correlation Filter (CF) based tracking methods using Convolutional Neural Network (CNN) features have achieved the state-of-the-art performance for object tracking. However, how to design an efficient deep CF based tracking method has not been well studied in the literature. To address this issue, we first develop a generic framework, which breaks a deep CF based tracking method into five components, including motion model, CNN feature extractor, CF model, CF updater, and location model. According to this framework, we design each component step by step. Then we propose a novel deep CF based tracking method by combining five effective components together. The proposed method outperforms several state-of-the-art tracking methods on two tracking benchmarks. Then the ablative experiments are conducted to study the influence of each component. The results show that the CF model and the CNN feature extractor play the most important roles in a deep CF based tracking method. Moreover, the CF updater, the location model, and the motion model can also improve the performance substantially.