Segmentation Of Moving Vehicles Research Articles

Moving Vehicle Segmentation Based on EM Algorithm And Fast Motion Estimation Algorithms

Moving vehicles segmentation is an interesting yet difficult problem in intelligent transportation system. Ordinary segmentation method is obstructed by some problems: the moving objects in dynamic scenes are the examples. Bayesian framework is utilized to classify the motion in the scenes to improve the robustness of the model and EM algorithm is used to estimate the parameters of the model. The direction and magnitude of the motion vectors are the inputs to the Bayesian rule and EM algorithm. In existing methods traditional Exhaustive search method is used for finding the motion vector. Computational cost of the Exhaustive search method is high. In this paper we are using some fast algorithms like Three Step Search and Four Step Search algorithms for reducing the computational cost. Experimental results on the scene,’ waving trees’, shows that the proposed model can segment the moving vehicles correctly with less computational cost. Quantitative evaluations demonstrate that the proposed method outperform the existing methods.

Moving vehicles detection based on adaptive motion histogram

As one of the important topics in computer vision, moving vehicle segmentation has attracted considerable attention of researchers. However, robust detection is hampered by the interferential moving objects in dynamic scenes. In this paper, we address the problem of the moving vehicles segmentation in the dynamic scenes. Based on the distinct motion property of the dynamic background and that of the moving vehicles, we present an adaptive motion histogram for moving vehicles segmentation. The presented algorithm consists of two procedures: adaptive background update and motion histogram-based vehicles segmentation. In the adaptive background update procedure, we make use of the lighting change of the scene and present a novel method for background evolving. In the motion histogram-based vehicles segmentation procedure, an adaptive motion histogram is maintained and updated according to the motion information in the scenes, and the moving vehicles are then detected according to the motion histogram maintained. Experimental results of typical scenes demonstrate robustness of the proposed method. Quantitative evaluation and comparison with the existing methods show that the proposed method provides much improved results.

Moving vehicles segmentation based on Bayesian framework for Gaussian motion model

Accurate moving vehicles segmentation is an interesting yet difficult problem in intelligent transportation system. Robust segmentation is obstructed by some problems: the interferential moving objects in dynamic scenes, the vibration of cameras and the change of lighting conditions are all examples. In this paper, we propose Gaussian motion model for moving vehicles segmentation in the dynamic scenes. By investigating the distinction between the motion vectors of the dynamic background and those of the moving vehicles, we find the fact that the motion vectors of the moving vehicles cluster in a small region while those of the dynamic background are dispersive. Gaussian motion model is then proposed to model the motion of the moving vehicles and that of the dynamic background. Bayesian framework is utilized to classify the motion in the scenes to improve the robustness of the model and EM algorithm is used to estimate the parameters of the model. Experimental results on typical scenes, such as waving trees, camera vibration, snowfall and fog, show that the proposed model can segment the moving vehicles correctly in the complex scenes. Quantitative evaluations demonstrate that the proposed method outperform the existing methods.