Method for tracing a plurality of human faces base on correlate vector machine to improve learning

Abstract

The invention relates to the computer vision technology field, and provides a multi-human face tracking method based on the relevant vector machine. The method which can improve the studying quality comprises the following steps that: initialization detection is carried out to a scene, and the detected human face is constructed with a human face motion model and a color model, which are stored into a human face model database; at the same time, the state of the detected human face is initialized, and then is recorded into a human face state database; during the multi-human face tracking process, different tracking methods are adopted according to the different states of the human face, and detection is carried out to the tracking result, so as to change the state information of the human face according to the detection result; during the tracking process, a whole image searching is carried out once a plurality of frames, so as to detect the human face which is failed in being tracked and the new human face which enters into the scene. The invention requires no manual intervention, and can simultaneously detect and track random multi-human faces at a quick operating speed, thereby satisfying the real-time processing requirement.

Description

technical field [0001] The invention relates to a method in the technical field of telecommunications, in particular to a multi-face tracking method based on a correlation vector machine and boosting learning. Background technique [0002] Detecting and tracking targets in image sequences is one of the important research directions of computer vision. It has a wide range of applications in intelligent visual monitoring, advanced human-computer interaction, video coding, etc. At the same time, effective target tracking is also the basis of behavior understanding. . Tracking is equivalent to creating a corresponding matching problem based on position, speed, color, texture and other features between consecutive frames. In the face tracking method, the common idea is to use skin color-based tracking, motion-based tracking and local feature-based tracking. tracking. Such methods usually have high computational complexity, and most of them require manual initialization, so they...

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Problems solved by technology

The article "A sparse probabilistic learning algorithm for real-timetracking" published by O.Williams et al. in "The Ninth International Conference on Computer Vision" (the Ninth International Conference on Computer Vision) in 2003 (a real-time tracking algorithm using sparse probabilistic learning) Tracking method) proposes to track by using the correlation vector machine to learn the regression function between the target and the motion. This method has high computational efficiency and tracking accuracy, but this method can only track a single face

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