  Visual object tracking is an important task in computer vision, which has
many real-world applications, e.g., video surveillance, visual navigation.
Visual object tracking also has many challenges, e.g., object occlusion and
deformation. To solve above problems and track the target accurately and
efficiently, many tracking algorithms have emerged in recent years. This paper
presents the rationale and representative works of two most popular tracking
frameworks in past ten years, i.e., the corelation filter and Siamese network
for object tracking. Then we present some deep learning based tracking methods
categorized by different network structures. We also introduce some classical
strategies for handling the challenges in tracking problem. Further, this paper
detailedly present and compare the benchmarks and challenges for tracking, from
which we summarize the development history and development trend of visual
tracking. Focusing on the future development of object tracking, which we think
would be applied in real-world scenes before some problems to be addressed,
such as the problems in long-term tracking, low-power high-speed tracking and
attack-robust tracking. In the future, the integration of multimodal data,
e.g., the depth image, thermal image with traditional color image, will provide
more solutions for visual tracking. Moreover, tracking task will go together
with some other tasks, e.g., video object detection and segmentation.
