Intelligent transportation moving object detection tracking method

Abstract

The invention discloses an intelligent transportation moving object detection tracking method which is used for detecting and tracking a moving object in an intelligent transportation video. The intelligent transportation moving object detection tracking method comprises the following steps: considering objects with obvious edge characteristics such as guard bars in the middle part and on the two sides of a road in a video frame image; adopting a finite difference first-order partial derivative matrix to calculate the gradients of an input front video frame image and a back video frame image, and extracting a fine outline of an input image characteristic area; performing outline connection, area filling consolidation and morphological processing to obtain a preprocessed image; adopting an improved SIFT algorithm to extract key points from the preprocessed image, and only establishing a six-layered Gaussian pyramid for the preprocessed image, so as to ensure the situation that a few of key points can be detected when the image is changed in a small scale, meanwhile reduce redundant calculation requirement on establishing the pyramid, and improve the time efficiency of the algorithm; before frame difference, the image of the video sequence front frame and back frame is rectified by utilizing an affine transformation model obtained by the preprocessed image, so as to eliminate the influence of the video frame shift on performance detection.

Description

technical field [0001] The invention relates to a moving target detection and tracking algorithm for intelligent traffic video monitoring system data, belonging to the technical field of image processing. Background technique [0002] Intelligent transportation system has a wide range of application prospects due to its all-day, all-weather, and large monitoring range. The basic technologies of intelligent video surveillance include moving object detection, moving object classification, moving object tracking, etc. Currently, the research on these technologies has attracted much attention. [0003] The existing moving target detection methods mainly include: adjacent frame difference method, optical flow segmentation method, background subtraction method and so on. The inter-frame difference method is to subtract the adjacent frame images, then take the threshold and segment the resulting image, and extract the moving target. The moving object detection algorithm based on ...

AI Technical Summary

Problems solved by technology

However, due to the influence of shooting conditions, weather and human factors, there is a drift in the background of different frames of the captured video image, although the magnitude of this drift may be very small, and even the human eye can hardly notice it.

However, this effect has an irreparable impact on the results of inter-frame differential detection of moving targets, which seriously affects the detection and tracking performance of the algorithm.

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