Visual tracking using multi-stage random simple features

In recent years, deep models offer a promising solution to extract powerful features. Motivated by the effectiveness of the Convolutional Networks (ConvNets) model in image classification and object detection, we present a visual tracking algorithm using the ConvNets model to extract multistage features. The key point of this paper is to show that the multi-stage features extracted by the ConvNets are very proper for visual tracking. In addition, we design a general procedure to generate simple rectangle filters with different complexity, and employ the rectangle filters to construct the ConvNets. The computational cost is reduced by using integral images. The filters are kept constant, thus the update of our tracker would not cost much time. The tracking is formulated as a binary classification problem, and we use an online naive Bayes classifier to build our tracker. The experimental results demonstrate that our tracker achieves comparable results against several state-of-the-art methods.