# Multiple Object Tracking in Robotic Applications: Trends and Challenges
## 1 Introduction
### Related Work
The second is filtering-based tracking, where Kalman and Particle filters are discussed. The authors in Ref. [13] comprehensively surveyed the deep learning-based methods for MOT. They also provided an overview of the data in MOTChallenges and the type of conditions included. An evaluation of the performance of some methods on this dataset is then listed. In Ref. [14], the deep learning-based methods for MOT were also reviewed. Similarly, the authors alsoprovided an overview of the benchmark datasets, including MOTChallenges and KITTI, and presented the performance of some methods. In Ref. [23], the vision-based methods used to detect and track vehicles at road intersections were discussed. The authors categorized those methods depending on the sensors used and the approach carried out for detection and tracking. On the other hand, the authors in Ref. [24] presented methods introducing vehicle detection and tracking in urban areas, and an evaluation was then discussed. UAVs' role in civil applications, including surveillance, have been surveyed in Refs. [25; 26]. The authors discussed the characteristics and roles of UAVs in traffic flow monitoring. However, there have not been many contributions to vehicle tracking methods using an UAV. The authors in Refs. [7; 8], provided a detailed overview of the types of sensors mounted on autonomous vehicles, such as LIDAR, Ultrasonic, and cameras, for data perception. They also surveyed the current advancement in the autonomous driving field commercially and the type of technology associated with that. In Ref. [21], the authors studied the role of deep learning in autonomous driving including perception and path planning. In addition to deep learning approaches, a general review was introduced in Ref. [27]. In Ref. [28], the methods used to extract and match information from multiple sensors used for perception were reviewed. They also discussed how data association could be an issue in using multiple sensors to achieve reliable multiple object tracking. The authors in Ref. [29], surveyed the methods that utilize LIDAR in data perception and grouped the performance results on the KITTI dataset. Ref. [22] provided a comprehensive overview of the KITTI dataset's role in the autonomous driving application. The dataset can be used for training and testing pedestrians, vehicles, cyclists, and other objects that can be found on the road. Moreover, the dataset was extended to lane and road marks detection by Ref. [30]. Although the techniques mentioned above were very thorough in reviewing techniques, we aim in this paper to provide comprehensive research that surveys the techniques associated with autonomous robotics applications, provides an insight into the different tracking methods, gathers and compares the results from the different methods discussed in the paper, and evaluate the current work and find limitations that require future research. Table 1 lists the recent reviews, the year of publication, and the datasets used for comparing MOT methods. Section 2 discusses the state-of-art methods and techniques introduced by the literature. Section 3 discusses the benchmark datasets and evaluation metrics popularly used by the research for training and testing. Section 4 presents the evaluation results collected from the literature and discussion. Finally, Sections 5 and 6 provide the current study challenges and the future work that is required.