Bayesian Human Recognition Across Multiple Cameras in Crowded Situations
基于贝叶斯模型的相机间人群目标识别

Getting exact human position under occlusion is a key problem to object recognition across multiple cameras with overlapped views.The problem of human segmentation was converted to maximize the posteriori estimation by constructing a human model and a Bayesian model. And then the same objects were matched in different views on the least distance principal by taking the human axis as a feature. Experiments show promising results on human segmentation and recognition in crowded situations and the accuracy rate is high.