An Overview of Correlation-Filter-Based Object Tracking

In the last two decades, object tracking has been one of the prevalent fields in social media. Object tracking uses a dynamic model to track the same target, aiming to analyze the same social object and its behavior in a set of consecutive video frames. In this article, we provide a comprehensive survey on some representative and latest correlation-filter-based object tracking methods and compare their respective strengths and weaknesses under the theoretical and experimental analyses. First of all, we divide these correlation-filter-based object tracking methods into four categories according to their characteristics, including categorized features, space weight factors, scale factors, and expert strategies. Second, extensive experiments on benchmark datasets with 11 tracking challenges are performed and discussed. Finally, this article also attempts to provide some insights for the readers to comprehend and improve the involved tracking algorithms. It is expected that the overview would present a detailed framework and some enlightenments of object tracking to readers who are new in this area.