Moving Objects Segmentation And Its Application

As a key supporting technique for computer vision,moving object segmentation has far-going pragmatism significance and application importance.This dissertation presents some efforts on extracting moving objects from monocular videos captured by static camera,and main contributions of my work include:(1) Mean shift based background modeling and moving objects segmentation algorithm.Mean shift based non-parametric background modeling supports more sensitive and robust detection in dynamic outdoor scenes.This algorithm aims to deal with the limitation of high computational complexity.Firstly,fast mean shift approach is presented according to temporal dependencies.Secondly,coarse to fine method is proposed to avoid raster scanning entire image.Foreground pixels are detected in coarse level to roughly locate the foreground objects in the image,and then fine detection is performed on the corresponding blocks gradually.(2) A background model based on pixel layer for moving objects segmentation.Fast mean shift approach is used to cluster into layers those pixels that share similar statistics.The background is then modeled as a group of pixel layers.An incoming pixel is detected as foreground if it does not adhere to these layer-models of the background.The proposed method performs better than the tradtional MoG method under temporally irregular dynamic textures.(3) A moving objects segmentation algorithm based on graph cuts.The background model is represented as a group of pixel layers,and the foreground and shadow models are learned from background subtraction.We design a histogram based method to estimate darkening ratio caused by moving shadow so as to model the shadow more accurately.Markov Random Field is used to model the dependencies among neighbouring pixies,and the final foreground segmentation is subsequently achieved by the graph cuts algorithm.We also developed an automatic video surveillance system for marine scenes.It accesses the video streams of marine scenes transferred through Internet and performs moving objects detection and tracking to discover the prohibited objects and alarm acording to user-defined rules.The system supports real time monitoring as many as eight channels of video stream on personal computer.