# Multiple Object Tracking in Robotic Applications: Trends and Challenges
## 1 Introduction
### Related Work  
The authors in Ref. [20] provided a summary of the techniques developed for SLAM-MOT (combination of SLAM and MOT systems) that utilize the dynamic features to construct 3D object tracking and multi-motion segmentation systems. In Ref. [20], 3D tracking of dynamic objects techniques was categorized into trajectory triangulation, particle filter, and factorization-based approaches. The authors in Ref. [20] discussed the data fusion problem in autonomous vehicles. The perception of data from different sensors such as RGB cameras, LIDAR, and depth cameras provides more knowledge and understanding of the surrounding environment and increases the navigation system's robustness. In Ref. [20], a survey is conducted on the techniques used for SLAM in autonomous driving applications and the limitations of the current research. The evaluation in this paper was done on the KITTI dataset. The authors in Ref. [12] categorized the MOT approaches into three groups. The first is the initialization method, which defines whether the tracking would be detection-based or detection-free. Detection-based tracking or tracking-by-detection is the most common, where a detected object is connected to its trajectories from future frames. This connection can be applied by calculating the similarity based on appearance or motion. Detection-free tracking is where a set of objects is manually localized in the first frame and tracked through the future. This is not optimal in case new objects appear and is rarely applied. The second is based on the processing mode, either online or offline tracking. Online tracking is where objects are detected and tracked in real time. This is more optimal in the case of autonomous driving applications as offline tracking is where a batch of frames are processed at a low FPS. The final one is the type of output where it can be stochastic, in which the tracking varies at different running times, or deterministic, in which the tracking is constant. They would further define the components that are included in the MOT system. Appearance model is used to extract spatial information from the detections and then calculate their similarity. The visual features and representations extracted can be defined either locally or regionally. Motion models are used to predict the future location of the detected object and hence, reduce the inspection area. A good model would have a good estimation after a certain number of frames as its parameters are tuned towards learning how the object moves. Linear (constant velocity) and non-linear are the two types of motion models. Although there has been a rapid advancement in multiple object detection and tracking for autonomous driving, it is still processing only a few objects. It will be a giant leap forward to have a system capable of tracking all types of objects in real time. This can be achieved by generating a great deal of data that tackles the problem at different perceptions such as camera, LIDAR, ultrasonic, etc. [21]. The issues related to the full deployment of MOT in autonomous vehicles lie in that its reliability heavily depends on many parameters, such as the camera view and the type of background (dynamic or static). This leads to difficulty in being entirely trustful towards MOT in different real scenarios and environments [12]. Tracking pedestrians is a far more difficult task than tracking vehicles whose motion is bounded by the road compared to the motion of people, which is very random and challenging for the system to learn. Another issue is the occlusion, which leads to high fragmentation and ID switches due to losing and re-initializing tracks every time they get lost. There have been very few systems that comprehensively tackle the problem, which leaves a huge space for improvements [22]. In Ref. [16], the tracking algorithms are categorized into two groups. The first is matching-based, which defines how features, such as appearance and motion, are first extracted and used to measure the similarity in the future frames.