In the scene of large crowd gatherings and challenging visiting places such as rough hills and high glass buildings, acquisition of the images through normal cameras is difficult and next to impossible. In all such scenarios, the drone becomes a useful acquisition sensor to capture the detailed information of the scene and the objects present there. With the rapid development of consumer unmanned aerial vehicles (UAV) or drones, the utilization of these devices became extremely easy. The popular use-case of the drones can be seen for the surveillance to identify any possible threats in the large crowd gathering and recognize the different individuals present in the crowd. However, the images captured using the drones are generally taken from a significant distance to avoid any collision; hence these images generally suffer in quality such as low resolution, motion blur and other environmental factors. The impact of these artifacts has been seen in the face recognition performance using several machine learning algorithms on large-scale drone databases namely Drone SURF. In this research, we intend to tackle the above artifacts by looking at the problem from the perspective of super-resolution of low-quality images. We have studied state-of-the-art (SOTA) super-resolution algorithms and see whether current methods are capable of handling the challenges of drone images. Apart from that, we have also evaluated another SOTA deep network developed for object detection for human segmentation in drone images. The proposed research provides interesting findings highlighting the limitations of existing works from the perspective of handling drone images. We would like the readers to go through the paper to find out the current limitations and possible future directions in drone image surveillance.