Movement target tracking algorithm in intelligent vision

Abstract

The invention discloses a movement target tracking algorithm in intelligent vision. The method is characterized by comprising the following steps of 1, conducting segmentation on an f (x, y) image, and calculating the best threshold value Td, wherein f (x, y) is the pixel grayscale of the original image in a video; 2, setting a kth-frame image in the video at a t moment as fk (x, y) and setting ak-1th-frame image as fk-1 (x, y), wherein the frame difference between the two frames of images meets the condition that Dk (x, y) =|fk (x, y) - fk-1 (x, y)|; 3, conducting threshold value segmentation on the frame images in the video, and extracting a movement target fobj (x, y) from video frames, wherein the movement target is shown in the description; 4, locating the movement target extracted in the step 3; 5, conducting target tracking on the located target in the video.

Description

technical field [0001] The invention relates to the technical field of target tracking, in particular to a moving target tracking algorithm in intelligent vision. Background technique [0002] The research and application of moving target positioning is an important part of intelligent video perception. It has attracted the attention of more and more experts and scholars at home and abroad. Many target detection and tracking algorithms have also been proposed. At present, according to different positioning methods, it can be generally classified as The following categories: feature-based tracking, active contour-based tracking, and 3D-based tracking. Typical studies include: Daniel proposed a positioning and tracking method based on moving target features, which used the color features of the target to distinguish and track multiple targets in the scene; The accurate and complete segmentation of the occluded moving target, combined with the extended Kalman filtering trackin...

AI Technical Summary

Problems solved by technology

Aiming at the problem of insufficient accuracy of traditional edge-based 3D vision tracking, Zhang et al. proposed a 3D tracking based on corner features to achieve precise tracking of 3D vision.

[0003] Although the various methods mentioned above have their own characteristics and advantages, they also have their own limitations, especially for specific application fields, such as: complex scenes such as coal mines and parking lots. Compared with ground video surveillance, due to space Limited, there are uneven illumination, large changes in background illumination intensity, and even dust, humidity, occlusion and other factors, resulting in a serious decline in the quality of surveillance images, so it is relatively difficult to perform image segmentation and target positioning in these complex environments , using traditional target positioning algorithms to deal with generally can not get ideal calculation results, and it is difficult to achieve real-time tracking of targets, hindering the in-depth development and application of intelligent video surveillance

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