# A Robust Visual Tracking Algorithm Based on Spatial-Temporal Context Hierarchical Response Fusion
## 1 Introduction  
Visual object tracking is a basic task in computer vision, with a wide range of applications such as autonomous driving, robotics, video surveillance, human-machine interaction and so forth [1; 2]. Although the initial frame of the target is given, how to use an effective method to judge the position of the target in the subsequent frame is a difficult problem. These methods should be able to overcome various challenges well, including background clutter, illumination changes, scale variation, motion blur, and partial occlusions. In recent years, Discriminative Correlation Filter (DCF) based tracking methods have shown prominent results on object tracking benchmarks [3; 4; 5; 6]. The discriminative methods view the tracking task as a binary classification problem. During the tracking process, a binary classifier is learned online to distinguish the target and its surrounding background, and the learned classifier is used to classify the image blocks in the current image frame-mark whether the pixel belongs to the target or the background. The main goal is to find the area with the highest confidence for classifier, which is the target location, and to use the tracking result as a sample to update classifier. This method is alsocalled tracking-by-detection approaches. Our work will follow the DCFs tracking methods based on the tracking-by-detection framework. Moreover, deep convolutional neural networks (CNNs) have shown high performance in many tasks. Activations from the last convolutional layers have been successfully employ for image classification [7; 8; 9]. Features from these deep convolutional layers are effective in saving spatial and structural information of the object. [PERSON] et al. [10] proposed the use of hierarchical convolutional features in VGGNet [8] for visual tracking. The main tracking task is to extract and use the features of each convolution layer. On the one hand, shallow features can accurately locate the target, but its disadvantage is that it does not capture semantic information very well. On the contrary, the advantages of deep features can capture semantic information very well. The disadvantage is that it cannot describe exhaustive spatial details to locate the target. The semantic information has a great effect on the object after the appearance changes. After the appearance changes, the semantic information has a great effect on the tracking. Therefore, the deep features of CNNs play an increasingly important role in visual object tracking. In this paper, we learn the above mentioned methods to extract hierarchical convolutional features as feature representation. We learn a spatial-temporal context correlation filter on convolutional layers and employ these correlation response scores for fusion to estimate the location of the target. [PERSON] et al. [11] proposed a spatial-temporal relationship between the target and its surrounding context regions, indicating that the context information of the target and its surrounding background regions can effectively improve tracking results. [PERSON] et al. [12] showed that the correlation between spatial-temporal contexts can improve tracking accuracy and robustness. It is necessary to establish the spatial-temporal relationship between the target and its surrounding environment. Therefore, we employ a context-aware framework [13] based on discriminative correlation filter as our spatial-temporal context model. On this basis, we obtain a powerful filter that produces a high response value for the target image block and a near-zero response value for the context region. In order to estimate scale changes adaptively, we utilize the HOG feature as feature representation to train a discriminative correlation filter and estimate the desired object scale from the best score frame. HOG [14] feature is a feature descriptor that used for object detection in computer vision and image processing.