# Deep Directional Network for Object Tracking
## 1 Introduction  
Object tracking is an important part of computer vision. Object tracking has various applications, such as video surveillance, human-computer interaction and traffic monitoring. Therefore, object tracking technology has rapidly advanced in recent years. There are two primary current trends in object tracking: deep learning and correlation filtering. Without the support of large-scale datasets, the potential of deep learning in object tracking has not been developed. [PERSON] proposed the DLT [1] algorithm to introduce deep learning to this field for the first time. At that time, because there were no large-scale datasets dedicated to tracking, offline training of DLT was performed using the Tiny Images dataset [2]. This was the first time the researchers observed the effectiveness of deep learning. With the advent of large-scale video sequence datasets, such as VOT (Visual Object Tracking Challenges) [3] and OTB (Visual Tracking Benchmark) [4], a large number of effective deep learning algorithms such as MDNet [5] has emerged, and the use of deep learning in the field of tracking has grown explosively. Object tracking is a difficult task because of several challenging factors, such as illumination variation (IV), scale variation (SV), occlusion (OCC), deformation (DEF), motion blur (MB), fast motion (FM), in-plane rotation (IPR), out-of-plane rotation (OPR), out-of-view (OV), background clutter (BC) and low resolution (LR) [3]. Traditional object tracking algorithms mostly use artificial features. Artificial features, such as CN (Color Name) [6] and HOG (Histogram of Oriented Gradient) [7],cannot obtain deep semantic information of the object, are not sufficiently robust and are ineffective in overcoming challenges in object tracking. Certain correlation filtering trackers, such as C-COT [8], also abandoned traditional artificial features and turned to deep features that better express the target semantic information. Certain algorithms, e.g., CFNet [9] and DCFNet [10], even entirely abandoned correlation filtering, using CNN's convolution calculation instead of correlation calculations, and train a correlational deep network model to achieve tracking ability. The deep convolutional neural network (CNN) has been widely used not only in target detection, such as YOLO [11] but also recently in the field of object tracking. Compared to traditional features, a deep feature can express the semantic information of the target at a deeper level. Presently, deep learning object tracking algorithms primarily use offline training and online fine-tuning for network learning. However, due to the scarcity of computing resources, numerous network parameters and large computational complexity, most deep-learning target tracking algorithms are slow. Similarly, to the MDNet [5] algorithm, most deep learning tracking algorithms use particle sampling mechanisms during the tracking phase to determine the maximum probability of a positive sample strategy for target tracking, undoubtedly increasing the amount of network computation. In the field of object tracking, in a broad sense, a positive sample is the object we need to track, and a negative sample is the background information surrounding the target. But different algorithms will have different positive and negative sample criteria. The GOTURN [12] algorithm only uses offline training. During the online tracking phase, the frozen network weights are not updated. The search area of the current frame and the target position of the previous frame are input into the network. The network learns to compare these areas to find the target object in the current frame; however, this algorithm can no longer retrieve the target once the target has been lost. At that point, the background will be falsely tracked. In terms of scaling, DSST [13] requires not only a position filter but also a scalar filter. The training of multiple filters undoubtedly increases the complexity of the algorithm.