An Approach for Moving Object Detection Using Maximum a Posteriori
An approach based on maximum a posteriori is presented for moving object detection in complex video scenes.Firstly,a maximum a posteriori framework is created according to conditional random field model and Markov random field model.Then temporal dependencies of consecutive label fields and spatial dependencies within each label field are merged into this framework.The object detection method integrates both color and edge features by object probability model.Experimental results show that the presented approach can accurately detect moving objects.