Infrared Pedestrian Segmentation Through Background Likelihood and Object-Biased Saliency

Pedestrian segmentation in infrared images is a challenging problem due to low SNR and inhomogeneous luminance distribution. In this paper, we first introduce background prior and object-center prior into infrared pedestrian segmentation, and propose a robust and efficient saliency-based scheme, which aims to obtain the accurate pedestrian object. First, background likelihood is developed to abstract the object representation based on the Gaussian mixture model soft decomposition. Second, by combining the shape information and the infrared character of the pedestrians, kernel density estimation-based foreground estimation is proposed to obtain the saliency iteratively with better adaptable for the fuzzy contour of the infrared object. Third, pedestrian boundary weight is employed to integrate the above two saliency maps for more intact and accurate results. Finally, pedestrians can be easily segmented from the infrared images, through any existing segmentation methods, as simple as the Otsu threshold method, on the obtained final saliency map. Extensive experiments on real infrared images captured by intelligent transportation systems demonstrate that our saliency algorithm consistently outperforms the state-of-the-art saliency detection methods, in terms of higher precision, F-measure, and lower mean absolute error. The effectiveness of our proposed segmentation algorithm is also evaluated by comparisons with the existing infrared segmentation methods and yields more precise and intact pedestrian regions.