**7. Conclusions**

This paper aims to review the current trends and challenges related to the MOT for autonomous robotics applications. This area of study has been frequently researched recently due to its high potential and standards, which are difficult to achieve. The paper has discussed and compared the MOT techniques through a common framework and datasets, including MOTChallenges, KITTI, and UA\_DETRAC. There is a vast area left to explore and investigate as well as multiple approaches created by the literature that has the potential to build into reliable and robust techniques. A summary of the components utilized in the general MOT framework, including appearance and motion cues, data association, and occlusion handling, has been listed and studied. In addition, the popular methods used for data fusion between multiple sensors, focusing on the camera and LIDAR, have been reviewed. The role that deep learning techniques are utilized in MOT approaches has been investigated thoroughly using quantitative analysis to evaluate its limitations and strong points.

**Author Contributions:** Conceptualization, M.G., T.B. and A.G.; methodology, M.G., T.B. and A.G.; software, M.G., T.B. and A.G.; validation, M.Y., H.A., and M.A.; formal analysis, M.G., T.B. and A.G.; investigation, M.G., T.B. and A.G.; resources, M.G. and M.A.; data curation, M.G., T.B. and A.G.; writing—original draft preparation, T.B., A.G. and H.A.; writing—review and editing, M.G., T.B., A.G., M.Y., H.A. and M.A.; visualization, M.G., T.B., A.G. and M.A.; supervision, M.G. and M.A.; project administration, M.G. and M.A.; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This paper is part of a research project funded by the Office of Research and Sponsored Programs (ORSP) at Abu Dhabi University through a research fund (Grant number 19300564).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Abbreviations**

The following abbreviations are used in this manuscript:


#### **References**


**Jawad Yousaf 1, Huma Zia 1, Marah Alhalabi 1, Maha Yaghi 1, Tasnim Basmaji 1, Eiman Al Shehhi 1, Abdalla Gad 1, Mohammad Alkhedher <sup>2</sup> and Mohammed Ghazal 1,\***


**Abstract:** Unmanned aerial vehicles (UAVs) have emerged as a rapidly growing technology seeing unprecedented adoption in various application sectors due to their viability and low cost. However, UAVs have also been used to perform illegal and malicious actions, which have recently increased. This creates a need for technologies capable of detecting, classifying, and deactivating malicious and unauthorized drones. This paper reviews the trends and challenges of the most recent UAV detection methods, i.e., radio frequency-based (RF), radar, acoustic, and electro-optical, and localization methods. Our research covers different kinds of drones with a major focus on multirotors. The paper also highlights the features and limitations of the UAV detection systems and briefly surveys the UAV remote controller detection methods.

**Keywords:** Unmanned aerial vehicles (UAVs); detection technologies; radio frequency-based (RF); radar; acoustic; electro optical; hybrid fusion; controller detection
