# A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion
## 2 Related Works  
In Discriminative Correlation Filter based trackers, the filter is trained to predict the optimal response map by minimizing a least-squares loss for all circular shifts of a training sample. Since the complicated convolution operations can be converted into simple element-wise multiplication operations, DCF shows the advantage of high computational efficiency. Firstly, [PERSON] et al. (2015) proposed a tracker using minimum output sum of squared error (MOSSE) filter, which uses a grayscale template. [PERSON] et al. (2019) replaced the grayscale templates by HOG ([PERSON] et al., 2017) features and built on multiple channel features which further improved the tracking accuracy and robustness. [PERSON] et al. (2016) learned separate filters for translation and scaling. The role of the two filters is to locate the target and estimate desired scale of the target object, respectively. [PERSON] et al. (2019) incorporated context information into filter learning. [PERSON] et al. (2017) proposed the STAPLE tracker which combines DCF and color histogram based model. [PERSON] et al. (2016) introduced a spatial regularization component in the learning to punish correlation filter coefficients depending on their spatial location, which enhanced the robustness of tracking effectively. [PERSON] et al. (2019) proposed a large margin visual tracking method with circulant feature maps, which employed a multi-modal detection technique to avoid tracking drift. C-COT ([PERSON] et al., 2019) and [PERSON] ([PERSON] et al., 2014) adopted an implicit interpolation model to solve the learning problems in the continuous spatial domain, which enhanced the tracking accuracy. [PERSON] et al. (2019) introduced a new metric called the peak-to-clutter mean (PCM) and it provided sharp and high correlation peaks corresponding to targets. This method improves the efficiency of detection. [PERSON] et al. (2019) introduced class-associative spectral fringe-adjusted joint transform correlation (CSFJTC) based on joint transform correlation (JTC) and employed class-associative filtering, modified Fourier plane image subtraction, and fringe-adjusted JTC techniques to execute the object detection task. The performance of the detection was outstanding. To reduce the training time significantly for online training of the object, [PERSON] et al. (2019) proposed Progressively Expanded Neural Network (PENNet) tracker methodology and employed a modified variant of the extreme learning machine. To overcome these challenges, such as object structural information distortions and background variations, [PERSON] et al. (2019) proposed a Directional Ringlet Intensity Feature Transform (DRIFT) method, which utilized Kirsch kernel filtering for edge features and a ringlet feature mapping for rotational invariance. This method obtained accurate object boundaries and improvements for lowering computation times. [PERSON] et al. (2019) introduced a spatial alignment module, which provides continuous feedback for transforming the target from the border to the center with a normalized aspect ratio. This method can handle undesired boundary effects. [PERSON] et al. (2019) used an adversarial learning method to maintain the most robust features of the target objects and proposed a high-order cost sensitive loss to decrease the effect of easy samples. Visual representations are important for visual tracking. Most tracking algorithms have recently employed deep features extracted from convolutional neural networks CNN as feature representation. [PERSON] et al. (2016) used the deep features learned from CNN for representation based DCF framework, which improved tracking performances. [PERSON] et al. (2019) learned a feature extractor with convolutional neural networks from an offline training set for visual tracking. To handle long training times and a large number of training samples, DeepTrack ([PERSON] et al., 2019) employed a single CNN for learning effective feature representations of the target object by a purely online manner.