An efficient human face tracking method based on Camshift algorithm

A Face Tracking Method Based on Camshift and SCKF in Rapid Moving Process

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.668-669.1025 ◽

2014 ◽

Vol 668-669 ◽

pp. 1025-1028

Author(s):

Fu Cheng Cao ◽

Xiao Xue Xing

Keyword(s):

Nonlinear Effect ◽

Face Tracking ◽

Uniform Motion ◽

Square Root ◽

Robust Tracking ◽

Tracking Accuracy ◽

Tracking Method ◽

Camshift Algorithm ◽

Moving Process ◽

Localization And Tracking

Aiming at the problem of face tracking under rapid moving process, a fast and robust tracking method is proposed. The possible position of face detected by the Camshift algorithm in the next frame is predicted by the square-root cubature Kalman filte (SCKF). Then, the localization and tracking of face are got frames by frames. The experimental results show that: the use of SCKF to solve the nonlinear effect caused by non-uniform motion of face and overcome the target loss problem of the linear Kalman algorithm. The proposed method greatly improves the tracking accuracy of face in the process of rapid movement.

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Development of the joint attention with a new face tracking method for multiple people

2008 IEEE Workshop on Advanced robotics and Its Social Impacts ◽

10.1109/arso.2008.4653580 ◽

2008 ◽

Cited By ~ 4

Author(s):

Han-Pang Huang ◽

Jia-Hong Chen ◽

Hung-Jing Jian

Keyword(s):

Joint Attention ◽

Face Tracking ◽

Tracking Method

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Face and Eye Detection

Automated Face Analysis ◽

10.4018/978-1-60566-216-9.ch002 ◽

2011 ◽

pp. 5-44 ◽

Cited By ~ 1

Author(s):

Daijin Kim ◽

Jaewon Sung

Keyword(s):

Face Detection ◽

Facial Expression Recognition ◽

Face Image ◽

Face Tracking ◽

Expression Recognition ◽

Human Face ◽

Eye Detection ◽

Face Image Analysis ◽

Face Images ◽

The Face

Face detection is the most fundamental step for the research on image-based automated face analysis such as face tracking, face recognition, face authentication, facial expression recognition and facial gesture recognition. When a novel face image is given we must know where the face is located, and how large the scale is to limit our concern to the face patch in the image and normalize the scale and orientation of the face patch. Usually, the face detection results are not stable; the scale of the detected face rectangle can be larger or smaller than that of the real face in the image. Therefore, many researchers use eye detectors to obtain stable normalized face images. Because the eyes have salient patterns in the human face image, they can be located stably and used for face image normalization. The eye detection becomes more important when we want to apply model-based face image analysis approaches.

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