# Object-Tracking Algorithm Combining Motion Direction and Time Series
## 2 Related Works  
Online model update. The object-tracking algorithm of online model update generally necessitates the online fine-tuning of the network to suit the specific target. [PERSON] et al. [11] introduced the DLT algorithm by applying deep neural networks to target tracking. The algorithm performs unsupervised training on a stacked denoising autoencoder when offline and fine-tunes the model when online. During the tracking process, DLT extracts candidate regions and determines the target location based on the region with the highest confidence level. MDNet [10] is an algorithm that uses a multi-domain convolutional network, which divides the network into shared layers and domain-specific layers. The shared layers acquire the target commonality, while the domain-specific layers acquire the target characteristics. [PERSON] et al. [12] proposed the use of Convolutional Neural Networks (CNN) with a tree structure to locate targets by combining multiple convolutional networks based on linear weighting to reduce the impact of interference samples on model updates. During tracking, CNN nodes are added at certain intervals, while old nodes are removed to maintain network stability. In response to the limitation of using a large number of training samples in tracking, [PERSON] et al. [13] viewed the CNN training process as an integrated learning process by using each convolutional kernel that outputs the corresponding feature map as a baseline learner. [PERSON] et al. [26] proposed the BranchOut method, which uses a network similar to MDNet but is designed with multiple fully connected layer branches, each with a variable number of fully connected layers. This method ensures the representation capability of the target features. Offline training models. The online fine-tuning method can be computationally expensive, prompting researchers to investigate models based entirely on offline training. One popular algorithm that has received significant attention is the Siamese network-based object-tracking algorithm. The SINT algorithm [27] was the first to introduce the Siamese network to object tracking by sampling the search image, selecting candidate areas, and using the target template to calculate cross-correlation responses for all candidate areas. Similarly, the classic SiamFC algorithm [14] directly extracts the target template and search image features using the same convolutional network and calculates the position in the search image that is most similar to the target using cross correlation. The SiamRPN algorithm [16] converts the object-tracking problem into a classification and regression problem by using the Region Proposal Network (RPN) to classify and regress the template features and search image features. Meanwhile, the SiamCAR algorithm [18] uses an anchor-free and proposal-free strategy to perform classification and regression prediction on the target, which reduces hyperparameters and improves algorithm performance. Rotation of the target significantly decreases the tracking accuracy of object-tracking algorithms. To address this issue, the RE-SiamNets algorithm [28] incorporates rotation equivariance into the Siamese network model, which effectively mitigates the adverse impact of target rotation. The attention mechanism has been shown to be effective at integrating the features of the target template and the search image. The TMT algorithm [22] incorporates the Transformer's encoder and decoder into the Siamese network and uses the encoder to extract target template features and the decoder to fuse target template features with search image features. The TransT algorithm [21] enhances feature extraction and fusion by designing the Ego-Context Augment module and the Cross-Feature Augment Modules based on the attention mechanism. The VTT algorithm [29] fuses the features of the template and search image using the Siamese network, enhances global information using the Transformer, and then determines the tracking result using the Top-1 result of classification and regression.