## 1 Introduction  
Vision-based Multi-Object Tracking (MOT) methods analyze image sequences to establish object correspondences over the images [1, 2]. Multiple MOT methods have been proposed over the years and have been widely used in applications, such as surveillance [3], traffic monitoring [4], autonomous driving [5], and mobile robot navigation, including object collision avoidance [6] or target following [7]. However, MOT results may be affected by difficult problem configurations due to crowded environments or occluded objects, which leads to limitations in performance for such scenarios. Moreover, due to a large number of applications where MOT methods can be applied, the importance of MOT is high and remains a challenging topic in the research community [1, 2, 8]. Throughout the years, MOT tasks were mainly performed by the tracking by detection paradigm [9], where objects were detected by an object detector and fed to the object tracking method, which then dealt with the object association between previous frames and the present one. Most methods proposed [10, 11, 12] use a Kalman Filter (KF) as a motion module to predict the position of objects of interest in the current frame. On the other hand, with the emergence of Deep learning-based Neural Networks (DNNs) [13, 14], new state-of-the-art methods have been proposed in object vision-based tasks such as object classification [15], recognition [16], and tracking [11, 17, 18]. Therefore, to improve the object association step of tracking algorithms, Convolutional Neural Networks (CNNs) have been applied to extract object appearance features, which are used to compute similarity values between two objects' feature maps, extracted over two consecutive images. On the other hand, CNNs have also been used to locate objects to track consecutive images [19; 20]. MOT techniques can be employed to improve the motion planning behavior and safety on the navigation tasks of mobile robot platforms [6]. MOT techniques can also be an asset on assistive platforms for target-following tasks, where the platform follows a specific target (e.g., following a caregiver, reaching an object). Due to several types of impairments, there are a significant number of people unable to perform daily tasks. Hence, a particular type of assistive mobile robot, robotic wheelchair platforms, has been researched aiming to increase the autonomy and mobility of such users [21; 22]. Brain-actuated wheelchairs [21; 23; 24] have also received particular focus in research, with several promising techniques for severely motor disabled people who are unable to control a robotic platform by the conventional interfaces, such as joystick [21; 25]. With the advances in Brain-Computer Interfaces (BCI) and shared control methods, new paradigms of the brain-computer interaction that allow the user to choose his navigation target have been proposed. The new paradigms can represent potential goals of interest to the user's navigation (e.g., objects) and can be empowered by considering the tracked objects from MOT methods. Once the user selects its navigation target, a MOT method is required to ensure that robotic wheelchair platforms navigate towards that specific target. However, to endow a mobile robot to pursue an object as its navigation target, a robust visual perception module, including an object tracking method, is required. Moreover, to ensure a robust object tracking performance, detection and tracking should be performed frame-by-frame, which is time-consuming and can lead to the inability of performing MOT in real-time [9]. In this work, considering navigation tasks in assistive platforms, an evaluation study of two multi-object tracking by detection algorithms, SORT [10] and Deep-SORT [11], using new data association metrics [26], is proposed. SORT and Deep-SORT methods were proposed with a focus on real-time object tracking tasks, both achieving state-of-the-art results with a high frame rate.