## 2 Proposed MOT Method
### Appearance Feature Extracting Network  
A robust appearance feature can prevent the object from drifting to false detection, and can also distinguish the correct object from occlusion. As time goes, the appearance of the object may change gradually, especially for the occluded object. Traditional artificial feature extraction methods imitate the characteristics of human vision and extract features with specific physical meanings. These methods often divide the image into several regions and construct feature vectors or histograms according to the information of multiple local regions. However, they are not perfectly suitable for occlusion and other situations because the artificial feature extraction is not very comprehensive and relatively simple, which often makes it impossible for the tracking algorithm to accurately correlate the same objects of different frames. Therefore, we propose a convolutional neural network based on pedestrian re-identification [17; 18].  
Following the idea of combining traditional methods with the technology of deep learning, we segment the input image, and then pass all the slices as input into the convolutional neural network. As Figure 4 shows, the original image size was \(128\times 64\), which is the Global part. In addition, we extend the original image to two other copies, Local Cross and Local Horizontal. Local Cross divides the original image into four local regions with a size of \(64\times 32\), while Local Horizontal divides the original image into four local regions with a size of \(32\times 64\). The reasons for our design are as follows.  
Firstly, we design the Local Horizontal patch by referring to the longitudinal distribution of human body structure, like head, trunk, legs, and feet, so that the discriminant ability of our model for pedestrians is reasonably enhanced. Secondly, according to the shooting angle of the camera in the scene, pedestrians are often obscured by one or more parts, therefore they cannot be displayed  
Figure 3: Exemplary qualitative tracking results for the MOT15 benchmark dataset. (a–d) are from a set of training images and (–h) are from a set of testing images. Following # is the individual frame number in each image sequence. Different trajectories are illustrated in different colors, with different identities marked in white. The red boxes without identities are detections, some of which have not been associated with trajectory. The filled boxes are low-confidence trajectories, while the unfilled rectangles are high-confidence trajectories.  
completely in the image while Local Cross can help the model to enhance its recognition ability on the basis of the different local characteristics of pedestrians. Thirdly, when the global slice and the local slice pass through the convolution neural network, we concatenate their output vectors, which can combine the common area of interest of both the global and local information, further consolidating the model. By artificially designing different feature extraction regions and using neural networks to extract effective features relatively accurately, we can make better use of the advantages of traditional methods and deep learning methods. We will discuss our network structure and experiments in Section 3.3.