Visual object tracking is a key problem in computer vision area.It has a wide range of applications in the field of human-computer interaction

behavior recognition

robotics

and surveillance.Recently

visual object tracking has been widely applied to object tracking field due to its efficiency and robustness of correlation filter theory.A series of new advances have been introduced and much attention has been achieved.Correlation filter-based tracking methods have become a research interest in this field.These methods have received extremely compelling performance in recent benchmarks and competitions.This paper reviews the current research status of the tracking field to allow more researchers to explore the theory and development of correlation filter-based trackers (CFTs). First

the general framework of correlation filter tracking is introduced.The correlation filter theory is presented based on the general framework.Then

the classic CFTs

such as the kernelized correlation tracking

are described in three parts as follows：training

detection

and updating in detail.Furthermore

problems that often occurred in tracking task

such as feature representation and scale variation

are discussed.The scale estimation strategy in CFTs is further divided into four different categories depending on how they handle the scale variation；the categories are scaling pool-based strategy

part or patch-based strategy

keypoint-based strategy

and other methods based on their proposed models.In addition

the current research status is analyzed in three aspects

namely

model-based improvement

part-based tracking

and ensemble-based tracking.Future development trends are also presented in the discussion. Fifty video sequences from object tracking benchmark (OTB-2013) dataset have been adopted in the experiments to analyze the performance of 45 state-of-the-art trackers

including 14 representative CFTs.Comparisons are performed for these trackers using average center location error

average Pascal VOC overlap ratio

and median frame per second.Precision and success plots are presented simultaneously to evaluate the overall and attribute-based performances.In the overall performance evaluation

the top 15 trackers are provided

and 11 CFTs are observed

fully reflecting the superiority of these trackers.In the attribute-based evaluation

benchmark sequences are annotated with 11 different attributes

such as scale variation

occlusion

and deformation.Success plots of different attributes are presented

and the performance of CFTs and state-of-the-art trackers is discussed.Experiment results demonstrate that most CFTs not only perform favorably in comparison to the state-of-the art trackers in terms of accuracy and robustness but also satisfy the demand of real-time processing. The research on correlation filter theory achieves great improvements；it has extensive applications in the object tracking field.However

actual scenarios are usually complex

and targets often undergo large appearance changes

which easily influence the tracking performance.Object tracking remains a challenging task.Developing highly efficient and robust CFTs is considerably significant.Future studies can be conducted on balancing between the accuracy and efficiency

selecting appropriate features

and exploiting spatial structure of reliable parts.The application of CFTs in multi-object tracking and long-term tracking is also a valuable point.