Human detection method in infrared video images
Ying-Hong Liang · Infrared and Laser Engineering · 2009
Human detection in images is a challenging problem in the applications of machine vision, such as intelligent video surveillance and vehicle assistance driving. Because visible -image-based methods have difficulty in extracting moving objects from complex backgrounds, more research interests on human detection have been addressed in infrared video images. In this paper, a new method for detecting human in infrared video images was proposed. Due to the pixel values in infrared images could be approximately modeled by the univariate Gaussian distribution firstly, the univariate Gaussian model was used to detect the pixels with large intensity in images, then, the 2D histogram feature vector was utilized to detect objects, which combined a gray histogram feature and a projection histogram feature. Experimental results show the detection rate (DR) of the presented method is high, but the fault detection rate (FDR) is also slightly high for lacking of quantity and representativeness of negative -sample.Therefore,more research should be done to decrease the FDR.