Human Detection using Gradient Maps and Golden Ratio

Human detection is an important topic that can be used for many applications, it is mainly found in areas that required surveillance such as airports, casinos, factories, construction and mining sites. In this paper, a novel human detection method is introduced to extract the human figure from an input image without prior information or training. This method firstly uses a head and shoulder detection scheme based on curve detection with scaled gradient magnitude and orientation maps. It is then followed by a human body estimation scheme based on gap detection and golden ratio. Finally, the human figure is extracted through thresholding local gradient magnitude regions and horizontal filling. Tests on various images have shown that this method is capable of detecting and extracting human body figures robustly from different images.