Pedestrian segmentation in infrared images is a difficult problem for the defects of low SNR and inhomogeneous luminance distribution. In this paper, we propose a method which aims to obtain the accurate pedestrian segmentation through a background prior and boundary weight-based saliency. Background likelihood is firstly calculated as background prior to get an abstract representation for infrared pedestrian. Then, by considering the object-center prior, the object-biased Gaussian model is applied to derive the probability density estimation for pedestrians. Finally, the above two results are integrated with the boundary weight to obtain the final saliency map for infrared image, based on which pedestrians can be easily segmented. Experimental results on real infrared images captured by intelligent transportation systems demonstrate the effectiveness of the proposed approach against the state-of-the-art algorithms.
['Lu Li', 'Yu Zheng', 'Xiangzhi Bai', 'Fugen Zhou']
Background prior and boundary weight-based pedestrian segmentation in infrared images