When Deep Learning Models Have A Bird's Eye View Of Humans

Human features as objects of study have been widely used in various machine learning applications -- be it face detection, video surveillance or even the development of autonomous cars. In fact, the task of ascertaining human features becomes the major work of ML systems in these applications. However, in the case of video surveillance, capturing human figures at an aerial level, especially from a moving equipment, becomes very challenging. Due to factors such as video equipment alignment or lighting, the accuracy of detection in the system takes a hit significantly. In order to resolve these issues, researchers are now exploring deep learning (DL) in video surveillance.