Appearance changes detection during tracking

Correlation tracker has made a huge success in visual object tracking. However, it is mainly because that the tracker cannot catch the occurrence of appearance changes, tracking based on correlation filters often drifts due to the unexpected appearance changes caused by occlusion, deformation and background clutter. In this paper, we propose a new method to detect the case when the tracker encountered the unexpected appearance changes. This method uses the following points: 1) Filter response curve would decreases dramatically when target suffers heavy appearance changes. 2) Features extracted from deeper layers of convolutional neural networks (CNNs) have more semantics information and features extracted from shadower layers have more spatial information. Extensive experimental results on several public benchmark datasets show that the proposed method can deal with the appearance changes effectively.