Visual object tracking is a very important task in computer vision. This paper develops a method based on the convolutional neural network (CNN) and correlation filters for visual object tracking. To implement a superior tracking method, we develop a multiple correlation tracker. This paper presents an effective method to track an object based on a combination of feature hierarchies of CNNs. We combine several feature hierarchies and compute the more discriminative map to track the object. Firstly, the correlation filters framework is selected to build the new tracker. Secondly, three feature maps from the CNN, which are inserted into the correlation filters framework, are adopted to evaluate the object location independently. Finally, a novel method of feature hierarchies integration based on Kullback–Leibler (KL) divergence is adopted. Experiments on the different sequences are carried out, and the outputs reveal that the proposed tracker has better results than those of the state-of-the-art methods, and it has the ability to handle various challenges.

中文翻译：

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使用分层卷积特征模型和相关滤波器的高效对象跟踪

视觉对象跟踪是计算机视觉中一项非常重要的任务。本文开发了一种基于卷积神经网络 (CNN) 和相关滤波器的视觉对象跟踪方法。为了实现卓越的跟踪方法，我们开发了一个多重相关跟踪器。本文提出了一种基于 CNN 特征层次结构组合来跟踪对象的有效方法。我们结合了几个特征层次结构并计算更具辨别力的地图来跟踪对象。首先，选择相关过滤器框架来构建新的跟踪器。其次，采用插入相关过滤器框架的来自CNN的三个特征图来独立评估对象位置。最后，采用了一种基于 Kullback-Leibler (KL) 散度的新的特征层次集成方法。