Journal of Ubiquitous Computing and Communication Technologies
IRO Journals
Volume 5 — Issue 1 — March 2023

Improved Adaboost-Camshift Face Tracking System in Complex Background

https://doi.org/10.36548/jucct.2023.1.004
Xin-ni Li
Electronics and Communication Engineering, School of Electronic and Information Engineering, Liaoning University of Technology, Liaoning, Jinzhou, 121001, China
Ya-jun Wang
Electronics and Communication Engineering, School of Electronic and Information Engineering, Liaoning University of Technology, Liaoning, Jinzhou, 121001, China
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How to Cite

Li, Xin-ni, and Ya-jun Wang. 2023. “Improved Adaboost-Camshift Face Tracking System in Complex Background”. Journal of Ubiquitous Computing and Communication Technologies 5 (1): 48-64. https://doi.org/10.36548/jucct.2023.1.004.
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Keywords

— Face detection
— Face tracking
— Adaboost algorithm
— Camshift algorithm
— Kalman filter
Published: 28-03-2023

Abstract

With the rapid growth of science and technology, people pay more and more attention to pattern recognition and computer interaction. Therefore, in the last few years, face detection and tracking technology in video sequence has become a hot topic for people to study. Face tracking detection has a wide application prospect in human-computer interaction, intelligent monitoring, video conference and other aspects. In this paper, the problem of face tracking in video sequences is divided into two aspects: face detection and moving object tracking algorithm. In the face detection problem, the face detection based on Adaboost algorithm is described in detail, and the three-frame difference method is added to make the algorithm better and enhance the speed of face detection. In terms of moving object tracking algorithm, Camshift face tracking algorithm based on color histogram is adopted, which is not affected by the shape and size of the target and has good real-time performance. However, under the influence of color interference and occlusion, the algorithm will make tracking errors. Therefore, Kalman filter is introduced. The algorithm can directly delineate the candidate areas of face to be detected, so as to ensure the feasibility of face tracking. The simulation video image face tracking system is verified by Matlab software. The experimental results show that the system can accurately detect and track the faces in the video image sequence, not only in the simple background, but also in the complex background and multiple faces can also be well detected and tracked, and the tracking ensures real-time performance.

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