# Multiple Object Tracking in Robotic Applications: Trends and Challenges
## 1 Introduction
### Challenges  
To effectively perform object tracking, one must develop a robust and efficient model that the users can effectively use. This section aims to provide a comprehensive overview of the various challenges facing developing and optimizing such models.  
Figure 1: General framework of MOT systems. Visual and motion features of the detected objects at frame T are extracted and compared to those detected from previous frames. A robust data association algorithm would be able to match the features of the same objects. The final output of the system would be tracked with unique IDs identifying the multiple objects detected and tracked over the multiple frames.  
The first challenge a model must face is the quality of the input video [15]. If the model cannot process the video properly, it will require additional work to convert it into a clear form so that it can be used to detect objects. The classification system must first identify the objects that fall under a particular class. It then gives them IDs based on their shape and form, which raises the issue that objects of this class come in varying shapes and sizes [16]. After the objects are detected, they must be assigned IDs with a bounding box to identify them to ensure that the model can identify multiple similar objects in the coverage area. The next challenge is to identify the objects that are moving in the ROI of the camera. This phenomenon can cause the classification system to misclassify the objects or even identify them as new ones. Aside from the quality of the video input, other factors that affect the classification of objects are also considered. For instance, the illumination conditions can significantly influence the model's accuracy [12; 13; 14; 16]. The model may not be able to detect objects that are blunt with the environment or have background conditions. It also needs to be able to identify them at varying speeds. One of the most challenging issues in object tracking is the Occlusion issue, where the object movement gets interrupted by other objects in the scene [12; 13; 14; 16]. It can be caused by various factors such as natural conditions or the object's movement out of the camera's ROI. Another reason is that other objects might block the visual of the object if the object is in the camera's ROI. Therefore, the system must be trained to identify and track the objects in motion. It also needs to be able to re-identify the IDs of the captured images with the same ones already used by the cameras. Figure 2 shows an example of the occlusion issue. The yellow arrow follows one of the tracks that maintains its ID after experiencing full occlusion. The problem of occlusion is minimized in bird-eye view tracking [17]. However, other challenges arise, such as the low resolution of objects and misclassifications. Another obstacle is related to onboard tracking in self-driving applications. The issue is that the tracking process needs to be quick and accurate for an efficient assistant driving system. The FPS is one of the essential factors determining the tracking quality in this case [18].  
Figure 2: Preserving the ID during full occlusion. The frames are obtained from the MOT15 dataset [19]. The yellow arrow is pointing towards a track (**top image**) that experiences full occlusion (**middle image**). The objective of the MOT system is to preserve the ID of the track (**bottom image**) and matches the previously detected object with the reappeared one.