## 5 Conclusions  
In this research, we have proposed a novel tracking technique that integrates the virtual simulation platform VCE using the AirSim python client that performs in virtual realistic environment as a drone agent. In order to predict and track the objects and to learn the environment, we used a mutually integrated recurrent neural network-based DQN tracker that was trained with several virtual image-based video sequences. The AirSim simulation platform allowed us to test our model in a virtual environment, gather crucial feature information, and identify the object classes autonomously. AirSim API allowed us to test our DQN agent-based tracker easily by connecting it directly to the virtual drone simulation platform. Even though there are several challenges in terms of a three-dimensional virtual realistic model environment, our proposed model was successfully trained and achieved better performance with a recurrent prediction-based network integrated with an action decision technique. Our model can work autonomously  
Figure 10: Qualitative results of the proposed tracking algorithm with two object classes: person and car.  
in a virtual simulation model by applying deep RL agent solutions. Additionally, we tested our model with two different data sets, VisDrone2019 and OTB-100, and compared our model performance with recent state-of-the-art techniques. Testing evaluation showed that our proposed technique displayed better performance among recent methods, with 92.07% and 82.35% precision in VisDrone2019 and OTB-100 data sets, respectively.  
In our future works, we plan to improve the performance by using fine-tuning methodology and performing more simulation experiments with different weather conditions. Moreover, we plan to test our model with other open-source video sequences in order to compare it with other conventional RL-based DQN trackers.  
Conceptualization, J.-H.P.; funding acquisition, K.F., S.-H.L. and K.-R.K.; investigation, J.-H.P.; methodology, J.-H.P.; project administration, K.F., [PERSON]-H.L. and K.-R.K.; software, J.-H.P., K.F., S.-H.L. and K.-R.K.; supervision, K.F., [PERSON]-[PERSON] and K.-R.K.; validation, K.F., [PERSON]-[PERSON] and K.-R.K.; writing--original draft, [PERSON]-H.P.; writing--review and editing, [PERSON]-H.P., K.F. and [PERSON]-[PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Brain Korea 21 project (BK21).  
Not Applicable.  
The original VisDrone2019 and OTB-100 datasets are available online at [[https://paperswithcode.com/dataset/otb](https://paperswithcode.com/dataset/otb)]([https://paperswithcode.com/dataset/otb](https://paperswithcode.com/dataset/otb)) (accessed on 1 January 2022) and [[http://aiskyeye.com/download/](http://aiskyeye.com/download/)]([http://aiskyeye.com/download/](http://aiskyeye.com/download/)) (accessed on 1 January 2022). These datasets used for comparing with algorithm performance with recent state-of-the-art models.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education (2020 RII1A306659411, 2020R1F1A1069124) and MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2022-2020-0-01797) supervised by the IITP(Institute for Information & Communications Technology Planning & Evaluation).  
The authors declare no conflict of interest.