## 2 Related Work  
### Object Tracking  
Object tracking techniques have become a fundamental task in real-time video-based applications that require establishing object correspondences between frames [8]. In the literature, proposed tracking techniques fall in two main categories [29]: Single-Object Tracking (SOT) and MOT. In SOT approaches, the appearance of the single target is known a priori, while in MOT techniques, the aim is to estimate trajectories of multiple objects of one or more categories without any prior knowledge about their appearance or location targets. For MOT, an object detection step is required across frames [1]. According to [1], applying multiple SOT models to perform MOT tasks generally leads to poor performance, often caused by similarly looking intra-class objects. Recent advances in MOT literature have been focusing on two different approaches: tracking by detection and joint tracking and detection. Tracking by detection [10; 11; 12; 30], as presented in Figure 1, makes use of object detection algorithms to detect and classify objects before performing the object association. This approach simplifies the tracking tasks as an object association task over consecutive frames. Methods receive an array of measurements and output bounding boxes with their respective tracking ID. On the other hand, joint tracking and detection methods [9; 17; 19; 20] are able to detect and track objects in a single model. Generally, this approach uses visual appearance features of the object to track and locate it in the frames of interest. Joint tracking and detection techniques have become widely popular due to the emergence of the deep learning-based Siamese Networks [18; 31]. With the emergence of deep learning-based object detectors, tracking by detection has become the most popular approach in the MOT research community [2]. This approach  
Figure 1: Overview of the Kalman Filter based object tracking algorithms used in this work. takes the benefit of object location knowledge to generate an association model that would be able to associate objects over time. One of the first MOT methods found in the literature is Multiple Hypothesis Tracking [32], which calculates hypotheses over measurements to estimate if an object should be associated to a track, be considered as a new track, or if it is a mis-measurement. It uses the KF algorithm to estimate the object's states and a probabilistic distribution over hypotheses to associate measurements to tracks. Recent works also employ the KF algorithm, as a motion model, to improve the association of objects over time [10; 11; 12; 33]. [PERSON] et al. [10] proposed SORT, which is composed of a KF to estimate object states, and by the Hungarian [34] algorithm to associate the KF predictions with new object detections. A year later, [PERSON] et al. proposed an improvement of SORT, the Deep-SORT [11], by including a novel cascading association step that uses CNN-based object appearance features. The data association algorithm combines the similarity of the object appearance features with the Mahalanobis distance between object states and, at a later stage for unmatched states, uses the SORT's data association. Despite the usage of a CNN, the Deep-SORT method achieved a promising frame rate on the object tracking benchmarks. A method similar to Deep-SORT was proposed by [PERSON] et al., the MOTDT [12]. MOTDT uses a fully CNN-based scoring function for an optimal selection of candidates. Euclidean distances between extracted object appearance features also are used to improve the association step. Recently, [PERSON] et al. [33] proposed the GMT-CT algorithm that incorporates graph partitioning with deep feature learning. The graph was constructed through the extracted object appearance features, which was used in the association step to model the relationship between measurements and tracks with higher accuracy. With the growth of deep learning-based Siamese networks in the object tracking community, a new paradigm has been proposed [1]. [PERSON] et al.