SRGAN-TQT, an Improved Motion Tracking Technique for UAVs with Super-Resolution Generative Adversarial Network (SRGAN) and Temporal Quad-Tree (TQT)

Unmanned Aerial Vehicles (UAVs) are gaining significant popularity due to wide-scale applications in civilian and military use. Unmanned Aerial Vehicles are most commonly used for surveillance. Object tracking is one of the most important things that an autonomous UAV has to perform. However, the accuracy of the object tracking model degrades when the object fades away to some distance or if the input images have low resolution. High-resolution cameras are expensive and increase the overall cost of the UAV. The concept of SRGAN-TQT (Super-Resolution Generative Adversarial Network - Temporal Quad-Tree), an improved object tracking pipeline for UAVs in the presence of low-resolution cameras or distant objects, provides a cost-effective solution with enhanced accuracy to perform object tracking. Implementation of Super-Resolution - Generative Adversarial Networks (SRGANs) and Temporal Quad-Tree (TQT) along with state-of-the-art object detection algorithms serve as the backend of the pipeline. This approach uses Deep Neural Network-based GANs to upsample images precisely. Temporal Quad-Tree (TQT) is a motion tracking technique that is an extension of the popular Quad-Tree segmentation algorithm. The Temporal Quad-Tree algorithm is present to reduce the computational complexity and give a highly reliable tracking algorithm. This consequently omits the requirement of a high-resolution camera for UAVs while increasing the object tracking capability.