Moving vehicle tracking method capable of resisting strong shadow interference

Abstract

The invention discloses a moving vehicle tracking method for resisting strong shadow interference based on a non-subsampled shear wave domain zero tree structure, which is high in accuracy and robustness and has self-adaptive capability, and comprises the following steps of: after a video frame is converted into an HSV color space from an RGB color space, carrying out non-subsampled shear wave transformation; assuming that the transformation coefficient obeys Gaussian distribution, calculating a weighted mask of each scale by adopting a mean value and a standard deviation of the transformationcoefficient; according to the zero tree distribution characteristic of the multi-scale transformation coefficient, correcting a fine-scale weighted mask by utilizing a coarse-scale weighted mask, andcarrying out linear combination on the weighted masks of each scale and each color channel to obtain a public mask; calculating a self-adaptive segmentation threshold value by using a maximum entropymethod based on least square fitting, and binarizing the public mask; and determining a moving vehicle area in a voting mode, and tracking the target vehicle by adopting a mean shift algorithm.

Description

technical field [0001] The invention relates to the field of intelligent traffic video processing, in particular to a moving vehicle tracking method with high accuracy, good robustness, self-adaptive capability, and resistance to strong shadow interference based on a zero-tree structure in a non-subsampled shear wave domain. Background technique [0002] In the process of automatic tracking of vehicle targets by intelligent transportation systems, the static shadows produced by the surrounding static scenes will cause short-term changes in the characteristics of moving vehicles; while the moving shadows produced by moving vehicles will increase the proportion of vehicles in the video image , so that the target vehicle is integrated with the shadow, and it is easy to be misdetected as a part of the moving target. Therefore, it is generally believed that shadows are a disturbing factor to vehicle target tracking, segmentation and information statistics. Whether it is a static...

AI Technical Summary

Problems solved by technology

However, the key to these methods is to extract texture and edge information effectively, but wavelet transform can only optimally represent one-dimensional point singularity and two-dimensional straight line singularity along horizontal, vertical and diagonal directions, which is widely used in videos and images. There are straight and curved singularities in other directions but nothing can be done

Therefore, the above-mentioned method based on wavelet transform is still insufficient in describing edges and textures, which in turn restricts and affects the robustness of subsequent vehicle target tracking algorithms to shadow interference

[0006] Although scholars at home and abroad have proposed a variety of shadow detection and removal algorithms and applied them to the target tracking of moving vehicles, there is still no vehicle target tracking that can stably resist static or moving shadow interference and does not require human interaction. method

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