## 2 Related Work  
**Online multiple object tracker.** Strong motion models and appearance models are essential for online MOT methods due to the consideration of the optimal selection in the current frame without future frames. With the advent of the latest advanced object detectors [23; 24; 25], various MOT methods that link their tracking based on detection results have become popular. The work in [26] proposed a simple motion model based on a Kalman filter affected by the performance of the latest CNN (convolutional neural network)-basedobject detector. Furthermore, [27] proposed a model using a CNN-based appearance model for [26]. The authors in [28] proposed a method of classifying the correct detection candidates among the crowd and selecting the optimal detection candidate using the heatmap generation model. [29] adopted an RNN (recurrent neural network) to cope with the problem of the occlusion between objects by integrating the spatial and temporal information. The work in [30] improved model performance by integrating the information from the CNN intermediate layer to compensate for the information loss that existing tracking frameworks result from using only the last CNN information in a detector. In the situations of providing real-time services, dynamic or unexpected movements frequently occur, but the existing MOT methods do not often cope with such situations resulting in the poor associations. On the contrary, we apply a method to overcome the dynamic situations to the traditional online MOT frameworks and evaluate its effectiveness in the experiments.  
**GAN-based image inference model.** Generative adversarial networks (GAN) [31] propose an adversarial loss in which two competitors, discriminator and generator, compete and learn from each other. Deep Convolutional GAN (DCGAN) [32] designed a CNN-based generator for image inference using a GAN. It indicates that when using the adversarial loss for the image inference problem, the pixel distribution close to the actual data can be obtained, resulting in more realistic images compared to the autoencoder-based model. As a result, great progress in the image inference problem such as the style transfer [33; 34; 35], the super resolution [36], and deblurring [37]. In this paper, we implement an image restoration module for MOT by adopting and learning a GAN-based image inference model for the damaged image restoration.  
**Appearance embedding model.** Identifying whether two images represent the same person or not is accompanied by considerable difficulties due to the curse of dimensionality. In particular, identifying an unaware person adds to the difficulty. To overcome these problems, image embedding methods [38; 39] using CNN were proposed recently. These models can learn to represent the whole body or a part of the body as feature vectors with small dimensions, and after learning, they are able to extract features of people that are not involved in the learning. In particular, [39] proposed a learning method using the triplet loss which achieves great results in facial recognition. We try to use a face feature to relax the problem that occurs when only a body feature is used to implement the identification function. There are two problems with using body features only. Firstly, because the boundaries become blurred when the occlusion between multiple people occurs, the embedding results mixed with the features of several people can be extracted. Secondly, the objects may be wearing similar clothes, which leads to less differentiation. To alleviate these limitations, we propose an appearance model using face features with a low incidence of occlusion and high discrimination.