Multiwindow-based target tracking method

Abstract

The invention provides a multiwindow-based target tracking method, belonging to the computer vision technology. The method comprises the following steps: 1) reading in the image of the initial position of the target, constructing N-numbered tracking windows according to the target characteristics and measuring the compensation di between each tracker and the initial position of the target X0=(x0, y0); 2) reading in the next frame image and computing the new position Yi of each tracker on the current image frame; 3) computing the weight Wi of all the trackers; 4) compensating the new positions Yi of all the trackers on the current image frame by the compensation di and obtaining the value shown in the specification; 5) computing the target position parameter E(X) with the weight Wi of the trackers and the value shown in the specification; and 6) initializing all the trackers according to the target position parameter, updating the reference windows of the trackers and continuously reading in the next frame image, wherein each window corresponds to one tracker. The tracking method can improve the tracking performance of the system under the complex conditions of shading, lighting variation, etc and has strong robustness, and the target tracking result is free from control by the result of a certain tracking window.

Description

technical field [0001] The invention belongs to a target tracking method of computer vision technology, in particular to a multi-window-based target tracking method. Background technique [0002] Target tracking in video sequences is based on digital image processing and computer vision, and based on dynamic image processing technology. It has a wide range of application prospects, such as human-computer interaction, auxiliary medical diagnosis and intelligent video surveillance and other fields. At present, many researchers have proposed different solutions for video target tracking, including algorithms such as template matching, mean shifting and particle filtering. [0003] The target moves in a complex environment, and the features will be affected by factors such as illumination and camera angle, and the target may be blocked by other parts of itself or scene objects, so that the accuracy of the tracked target position is low, and these factors need to be removed The...

AI Technical Summary

Problems solved by technology

[0003] The target moves in a complex environment, and the features will be affected by factors such as illumination and camera angle, and the target may be blocked by other parts of itself or scene objects, so that the accuracy of the tracked target position is low, and these factors need to be removed The impact of the existing technology is very complicated

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