# Learning Spatial-Temporal Background-Aware Based Tracking
## 2 Related Works  
DCF based paradigms have been extensively employed in the field of visual tracking for a long time. [PERSON] et al. [26] first introduced the correlation filter theory to visual tracking and proposed a single-channel minimum error square sum (MOSSE) correlation filter. The method is chosen as a baseline tracker to expand and optimize from different viewpoints. [PERSON] et al. [27] proposed DCF with circulant structure kernels (CSK) and employed dense sampling to achieve high-efficiency tracking performance based on tracking-by-detection framework. [PERSON] [28] introduced a kernel trick into the DCF framework, and effectively extended the single-channel to multi-channel features to achieve real-time and robust tracking performance. In order to solve the difference of variance on the scale, [PERSON] et al. [29] proposed a IBCCF model, which can deal well with the aspect ratio variance in the tracking process, and effectively improve the tracking robustness and accuracy. In terms of feature fusion, [3] built an effective analysis framework of multiple clues based on DCF for visual tracking, and integrated deep convolution features and traditional hand-crafted features to increase the discriminative performance of the classifier. [PERSON] et al. [30] systematically analyzed the characteristics of deep and shallow level hand-crafted features and proposed a novel adaptive feature fusion improvement that measures robustness and preciseness of the tracker, which is performed by employing the complementary relationship between the deep and shallow features. To handle with the boundary effect problem, [PERSON] et al. [24] introduced spatial-temporal regularization based on SRDCF, which enable robustness against the boundary effect problem. [PERSON] et al. [31] proposed the ASRCF model to address the boundary effect problem, which is achieved by introducing adaptive spatial regularization module, and can learn the reliable filter coefficients make the filter gain robustness to complex appearance changes.  
Currently, deep convolution features are widely exploited in the field of visual tasks [32; 33; 34; 35], the combination of the DCF based paradigm and deep network model is a significant drift in the development of visual tracking. With the help of deep network features, deep semantic information is captured, improve the tracking accuracy of tracking instrument. Reference [19] proposed the CREST model, which considers DCF as a layer class convolutional neural network, and integrates feature extraction, the corresponding reflection image is formed, and the formed image is updated to the corresponding neural network to form the corresponding training port to achieve promising tracking results. [PERSON] et al. [36] incorporated geometric transformation a network architecture based on correlation filtering information, and introduced a spatial alignment module, which can deal effectively with a variety of complex appearance changes and geometric transformations. [PERSON] et al. [37] make the most of the rich optical flow information between consecutive frames to enhance the representation of features, and treated optical flow estimation, feature extraction, and correlation filter and tracking processes as special layers of deep networks to achieve high-efficient tracking performance. In this work, we combine traditional hand-crafted features with deep network features (VGG-Net) on a DCF based framework, which takes full advantage of the performance dominant position of multiple properties to further raise the accuracy and robustness of the tracker.