## 1 Introduction  
In recent decades, due to the academic potential and commercial value, multi-object tracking (MOT) has attracted increased attention in computer vision. MOT has been applied in many applications, such as video surveillance [1], intelligent driving, human-computer interaction [2], virtual reality [3] and biomedical imaging [4], etc. The main tasks of multi-object tracking are locating multiple objects, maintaining their identities, and yielding their individual trajectories given an input video, or an image sequence. However, in crowd scenes many problems occur such as the overlaps of objects, frequent occlusion, and drastic changes in appearance, which will lead to a significant decline in the speed and accuracy of the tracking algorithms. Each tracking algorithm itself contains a lot of parameters. How to adjust the parameters to make the algorithm adapt to more scenarios is also an urgent problem to be solved. According to the processing mode, multi-object tracking algorithms are usually classified into online and offline. The difference is that in processing the current frame, the online algorithms only rely on exploiting the information of the frames up to the current frame, while the offline algorithms can use both past and future frames. Generally, the offline algorithms can build the optimal trajectories set based on global information, but they often need higher computational complexity and larger memory capacity. On the contrary, the online algorithms can only infer the current optimal trajectory set based on limited information. Although their real-time performance is relatively high, they are more vulnerable to object occlusion, error detection, and other problems, which lead to erroneous trajectories. At present, in the area of pedestrian movement, most multi-object tracking algorithms are designed based on a tracking-by-detection framework. This kind of method is usually divided into five steps: first, detect objects in the current frame. Second, extract features of the detections. Third, measure the similarity between detections and existing trajectories. Fourth, associate detections and existing trajectories according to the similarity matrix. Fifth, manage all trajectories and remaining detections, such as initialization, update, termination, etc. With the rapid development of the object detection field, object detection algorithms are becoming more and more mature. In both deep learning methods [5; 6; 7] and traditional methods of extracting features manually [8] great progress has been made in accuracy and real-time. More recently, several emerging state-of-the-art methods have been published. [PERSON] et al. [9] applied a recurrent neural network (RNN) in modeling appearance, motion, and interaction information of objects to compute their similarity to detections. [PERSON] et al. [10] designed a multi-branch neural network to predict the confidence and location of objects. [PERSON] et al. [11] exploited the one-shot learning MOT framework based on an attention mechanism. Despite the accurate tracking performance on account of deep learning, the common disadvantage of this framework is low speed owing to the need for multiple complex networks. [PERSON] [12] proposed an online MOT tracker based on Gaussian Mixture Probability Hypothesis Density (GMPHD) in combination with a similarity convolution neural network (CNN), which also utilized pedestrian re-identification technology. The way it deals with trajectory states is similar to our network, yet there is still a difference in identities switches (IDS) and other metrics. Although the performance of object detection algorithms is improving constantly, it is still difficult to avoid the problems of false detection, missed detection, incomplete bounding boxes, etc. Because of the interference of these factors, it is not easy to obtain useful and reasonable appearance features when extracting from objects, which further makes the similarity measurement particularly difficult.