A multi-target positioning and tracking video monitoring method

Abstract

The invention discloses a multi-target locating and tracking video monitoring method, wherein the locating and tracking video monitoring method is suitable for the moving target where camera field of view is fixed, and there are small dynamic changes at background. Before the system works, firstly the background is trained by Gaussian mixture model, and then the background subtraction method is used to get the foreground image in the next frame of the video, followed by the accurate foreground object area, that is moving target location, is obtained according to the expansion and median filtering; detecting if there is any moving target,if there is moving object, the target location is determined according to the connected domain search, if there is no moving target, then the search turns to the next video frame; the tone space transformation of the target position region image is carried out, and the NTSC space tone diagram I and the HSV space tone diagram H are weighted to get the tone histogram, and the back projection of the image is further obtained, then Meanshift algorithm is used to precisely locate the target position. For the next video frame, the above calculation is repeated. The monitoring method can be used for moving target trajectory analysis, vehicle detection and traffic violations speeding and pedestrian flow detection.

Description

technical field [0001] The invention designs a multi-target positioning and tracking video monitoring method, which is suitable for the video monitoring of moving target positioning and tracking in which the field of view of the camera is fixed and the background has slight dynamic changes of objects. Background technique [0002] The Meanshift algorithm is one of the more effective target tracking algorithms at present. This algorithm uses the method of gradient optimization to realize the positioning and tracking of the tracking target, and has good adaptability to the deformation, scaling, rotation and other changes of the tracking target. The operation speed is also relatively fast. The Meanshift algorithm has a better tracking effect in the case of a single-tone tracking target and a low similarity between the background image and the tracking target color, but when there are foreground objects with similar colors in the surrounding environment, since the Meanshift algo...

AI Technical Summary

Problems solved by technology

The easiest way is to take the average value of the video image sequence, but this has many disadvantages. First, a large number of video images need to be input before calculating the background image. Second, the average video image cannot have foreground objects.

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