# Object-Tracking Algorithm Combining Motion Direction and Time Series
## 1 Introduction  
Object tracking has become one of the most important research areas in computer vision due to its wide range of applications in surveillance, video analysis, autonomous driving, drone photography, and traffic patrol [1; 2; 3]. The primary goal of object-tracking is to predict the position and size of a target in subsequent frames of a video sequence after the initial frame has been provided. With the advances in deep neural networks, the data-driven method has become mainstream for object tracking. Compared with the traditional filter-based method, the data-driven method achieves the best performance in terms of tracking accuracy [4; 5; 6]. Recently, researchers have paid more attention to designing stronger neural networks for target discrimination and incorporating detection modules to enhance the algorithm's long-term tracking ability [7; 8; 9]. Despite these approaches achieving significant performance, they are still challenged by deformation, occlusion, complex backgrounds, illumination, and scale changes. Object-tracking models can be simply classified into two categories: online training models and offline training models. Online training models, such as MDNet [10], DLT [11], TCNN [12], and STCT [13], design a classifier to distinguish the target from the environmental context by using large-scale image data to learn target commonality offline and positive and negative samples to learn target characteristics online during tracking. Offline training models extract the target template from the image first, and then locate the target by integrating the features of target templates and search images, which avoids updating the network model during tracking. One common method is using a Siamese network to project the target template and search image to another space, and then integrate the features to locate the target, as performed by SiamFC [14], SiamFC++ [15],SiamRPN [16], SiamBAN [17], SiamCAR [18], and SiamRCNN [19]. The other way relies on the attention mechanism [20] that finds the correlation between the target template and the search image for highlighting important features and suppressing irrelevant ones, as performed by TransT [21], TMT [22], STMTrack [23], DTT [24], and STARK [25]. However, these algorithms neglect the continuous motion of the target from frame to frame, which leads to the discontinuity of the predicted target position and affects tracking accuracy. Furthermore, previous methods lack attention to the reliability of tracking results, which plays an important role in the algorithm for discriminating the target. To address these challenges, we proposed a novel object-tracking algorithm that combines motion direction and time series. Our proposed approach addresses the lack of consideration of target motion in previous algorithms by adding direction information constraint. During the training process, the algorithm learns general patterns of target motion, which enable it to obtain the motion information of a specific target during tracking, leading to improved tracking accuracy. Additionally, we propose a tracking result scoring module that processes time series to analyze the correlation between current tracking results and historical targets. This module captures high-quality templates via the dependability of tracking results in facilitating template updates. Our approach is independent of any particular object-tracking model and can be applied to a range of algorithms. In this work, we combine the proposed algorithm with the STARK [25] to design an object-tracking algorithm that integrates motion direction and time series analysis. Our main contributions are summarized as follows:  
1. We add an additional constraint from the angle quantity to enhance the understanding of motion direction for the network. The additional directional guidance loss guides the network learned motion direction information, which effectively improves the tracking performance. 2. A tracking result scoring module (TRSM) is proposed to determine the reliability of tracking results.