Visual object tracking: Review and challenges

Abstract

Visual object tracking is a challenging research topic in computer vision. Numerous visual tracking algorithms have been proposed to solve this problem and achieved promising results. Traditional visual tracking algorithms can be categorized into generative and discriminative algorithms. Recently, deep learning based visual tracking algorithms attracted great attention from researchers due to their excellent performance. In order to summarize the development of visual object tracking, some studys have analyzed non-deep learning and deep learning visual tracking algorithms. In this paper, the most advanced tracking algorithms are comprehensively summarized, including both non-deep learning and deep learning based algorithms. First, traditional non-deep learning based tracking algorithms are categorized into generative and discriminative methods. The generative algorithms are summarized from three perspectives: kernel series, subspace series and sparse representation series, and the discriminative algorithms are summarized from two perspectives: correlation filtering series and deep features series. Then, deep learning based algorithms are divided into Siamese network series and Transformer series. Siamese network based algorithms are summarized from different innovation directions, and Transformer based algorithms are summarized from two perspectives: CNN-Transformer and Fully-Transformer. Moreover, the commonly used datasets and evaluation indicators are introduced in visual object tracking, as well as the results and analysis of representative algorithms. Finally, the challenges faced in visual object tracking were summarized and its future development trends were pointed out.