A Survey of Visual Attention Based Methods for Object Tracking

摘要

Visual tracking has been widely used in numerous applications, such as unmanned aerial vehicles, mobile robots and intelligent visual surveillance. Robust and real-time object tracking in complex scenes is a challenge task.Difficulties in tracking objects can arise due to changing appearance patterns of both the object and the environment,as well as factors such as background interference. Visual attention is one of the key mechanisms of visual perception which directs the processing resources to the visual data of the potentially most relevant, specially directs our gaze rapidly towards objects of interest in our visual environment and as a result humans can easily achieve stable object tracking.Therefore introducing the visual attention mechanism to the object tracking in complex scenes, will facilitate the realization of stable and humanoid tracking algorithms. This paper aims to review the state-of-the-art of visual attention based methods for tracking. Firstly, we introduce the basic concepts of visual attention and its representative computational models. Secondly, the relationship between visual attention and tracking is described. Thirdly, the attention-based visual tracking algorithms are classified into five categories and detailed descriptions of representative methods in each category are provided, and their pros and cons are examined. Finally, we highlight the advantages of attention-based tracking methods and provide insights for future.