Human segmentation of infrared image for mobile robot search

In the search robotics field, human target segmentation method plays a basic preprocessing step in the visual guidance. However, with the wide application of the infrared sensor on robot vision, traditional segmentation methods are facing more challenges of low-contrast, overlapping and blurring targets, and complex background. This paper introduces an infrared human segmentation approach that integrates the improved pulse coupled neural network (PCNN), the curvature gravity gradient tensor (CGGT) and the mathematical morphology to address these above problems. This approach starts with an improved PCNN segmentation model. Local dynamic synapse weights are designed to enhance the synchronous pulsing ability of the improved PCNN model with similar inputs, and a reformed threshold is conducted to guide the process of segmentation. Moreover, eigenvalues of CGGT are guaranteed in this model as linking coefficients, in order to capture the edges and details of human target more exactly in segmentation. Lastly, the segmentation result is repaired by morphology operators, to ensure the integrity of the target region and the independent noise removal. Experiments on 200 real infrared images captured by the mobile robot CQSearcher I, demonstrate that our method is superior over the other classic segmentation methods in both the subjective visual performance and the objective indicators of misclassification error and f-measure.