An Adaptive Vehicle Segmentation Algorithm Based on Symmetric Frame Differential Constraint
In order to achieve accurate detection of vehicle targets in traffic videos and improve the prevention effect of traffic accidents,the vehicle segmentation algorithm for traffic video with the constraint of symmetrical frame difference is proposed.Firstly,the idea of symmetric difference is used to differentiate 3 adjacent consecutive images twice,and a background selection and update method with mixed Gaussian model based on adaptive distribution constraint is proposed to initially extract and update the background of high-definition video.Jump degree and stability in the image are used to determine whether a pixel belongs to a false target point.The background selection update mechanism is proposed to detect foreground targets,and the adaptive distribution number mixed Gaussian model algorithm is used to improve the computational efficiency of the Gaussian mixture model for achieving accurate separation of foreground targets.Secondly,for the resulting image,a shadow detection and removal algorithm based on vehicle edge correction is proposed,and vehicle segmentation detection features are enhanced under the complex background.Then,the moving vehicle target in the result image is segmented and detected by self-adaptive threshold.Besides inter class variance,the cohesion of video foreground target class is considered as a criterion for threshold selection.The adaptive threshold selection algorithm with maximum variance ratio is adopted,and the stopped vehicle is segmented by background updating and filtering.Finally,the experimental result shows that the proposed method can segment vehicle targets with different traffic scenes completely and accurately.In addition,the proposed method effectively improve the detection accuracy of vehicle targets with different situations in traffic.
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