## 1 Introduction  
The multiple object tracking (MOT) problem aims to assign IDs to multiple detected targets and to estimate the trajectory of the object until each tracking target disappears. Recently, high-performance real-time MOT research studies are required for scenarios such as human-computer interaction, autonomous vehicles, and humanoid robots. For this reason, researches for improving real-time MOT performance such as [1, 2, 3, 4, 5] have been actively conducted. Existing MOT frameworks can be classified into two types, offline and online, depending on the temporal range of data to be considered [6]. The offline methods [7, 8, 9] consider the range from the past to the future, while the online methods [10, 11, 12, 13, 14] consider the range from the past to the present. In general, the offline methods perform better than the online methods by global optimization considering the future state, but they are not suitable for real-time tracking applications such as the previous scenario examples. The online MOT frameworks for real-time tracking are often applied in complex, dynamic, or unexpected situations, but overcoming tracking failures in these environments remains a challenge. Online MOT frameworks need the best data association in every frame because only current and past frames are considered. In the MOT problem, the data association generally means updating the state of an object being tracked by the collaboration of a motion model and an appearance model. The motion model compares the positional similarity between the current state of the tracking object and the current detection result. In this case, prediction methods such as Kalman filters [15, 16] or particle filters [17, 18] are used to predict the current position of the tracking object, and the appearance model compares the similarity of the appearance between the past state of the tracking object and the present detection result. In this case, a method such as visual embedding is used to effectively extract features from a noisy image. The dynamic movements of a tracking object and a camera in a real-time environment cause the reliabilities of the motion model and the appearance model to decrease. Motion models are negatively impacted by the complexity of moving the camera and objects separately. This is because the motion models of existing frameworks generally adopt linear functions, making it difficult for them to infer objects with nonlinear movements. Appearance models are negatively affected by motion blurs caused by dynamic movements. Because the surrounding pixel information is mixed, the detailed pixel representation, especially the texture information of the picture, is lost, and the outline of the object is blurred. This causes difficulties in distinguishing the boundary between the background and the object. The motion blur refers to a phenomenon in which pixels bleed due to the movement of the photographing object while the photosensor of the camera records an image [19; 20; 21]. The phenomenon occurs often when there is vibration caused by an uneven road surface, when the tracking target moves quickly, and when the camera mounted on the moving platform moves. As a result, the dynamic situation during multiple object tracking negatively affects the motion model and the appearance model. In this paper, we propose three methods to overcome the limitations of existing multiple object tracking in the event of dynamic movement. The first is to perform re-detection after the image restoration on the detection result whose reliability is lowered by noise. This makes it possible to calibrate ambiguous detection results that the detector could not screen. The second is to classify and restore the damaged area before the image is entered into the appearance model. This makes it easy to guess the intact state of a damaged image and match the state of the existing object that the appearance model remembers with the current detection of the state change.