Method for real time tracking individual human face in complicated scene

Abstract

The invention relates to a method for tracing the face of a single person in real time in complex scenes. The method comprises the following steps: (1) pretreating frames: linearly transforming the frames captured by a video and filtrating waves with Gaussian templates; (2) detecting the face by adopting complexion features: detecting the face by adopting the convergence of complexion in YCrCb colored space and morphologic operation of the face; (3) elimination of areas similar to the complexion: elimination of non-face areas similar to complexion in combination with the face area and the face geometrical features to get a perfect face area before the barycenter of the face area is computed with the perfect face area and the initial search area is set around the barycenter; (4) tracing the face: tracing the face by adopting the continuous adaptive mean deflection method with the face detection results as novel back projection drawings and features. In the method, the face is abstracted and tracked in combination with multiple features; the effects are stable and the complexity of computation is low. The method can be widely applied in the fields of video monitoring, man computer interaction interfaces and so on.

Description

technical field The invention relates to the fields of man-machine interface interaction and video monitoring, especially the real-time tracking of a single human face in complex scenes. Specifically, it is a continuous mean shift method that comprehensively considers multiple features and utilizes a new type of back-projection graph. Background technique In applications such as human-computer interaction and video surveillance, as one of the salient features for identifying personal identities, the detection and tracking of human faces will directly affect the overall performance of the system. For decades, countless scholars have done extensive research and discussion on the problem of face tracking, which are mainly divided into: methods based on face models, based on feature matching, based on motion information and based on skin color. However, due to the influence of many factors such as head rotation, lighting, shadows, and occluders, it has always been difficult to ...

AI Technical Summary

Problems solved by technology

However, in the process of performing histogram statistics on the search area by the H color channel, it is susceptible to the interference of background pixels in the search area, which affects the accuracy of calculating the probability of pixel-like skin color, and thus cannot achieve a good tracking effect

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