Abstract
With the revolutionary development of cloud computing and internet of things, the integration and utilization of “big data” resources is a hot topic of the artificial intelligence research. Face recognition technology information has the advantages of being non-replicable, non-stealing, simple and intuitive. Video face tracking in the context of big data has become an important research hotspot in the field of information security. In this paper, a multi-feature fusion adaptive adjustment target tracking window and an adaptive update template particle filter tracking framework algorithm are proposed. Firstly, the skin color and edge features of the face are extracted in the video sequence. The weighted color histogram are extracted which describes the face features. Then we use the integral histogram method to simplify the histogram calculation of the particles. Finally, according to the change of the average distance, the tracking window is adjusted to accurately track the tracking object. At the same time, the algorithm can adaptively update the tracking template which improves the accuracy and accuracy of the tracking. The experimental results show that the proposed method improves the tracking effect and has strong robustness in complex backgrounds such as skin color, illumination changes and face occlusion.
Highlights
Target tracking technology is an important computer vision research field, which is widely used in the Internet of Things and artificial intelligence [1,2]
This section will mainly describe the human face features, which are utilized in tracking human face of interest by combining color feature and edge feature
A face tracking algorithm based on adaptive fusion of skin color and edge features is proposed, which adaptively updates the template and adaptively adjusts the target tracking window to adapt to complex video background
Summary
Target tracking technology is an important computer vision research field, which is widely used in the Internet of Things and artificial intelligence [1,2]. Face recognition technology can effectively realize real-time multi-objective online retrieval and comparison in crowded areas such as banks, and the actual application effect is good. Two excellent algorithms, namely mean shift [4] and particle filter [5], have been widely used for target tracking. The particle filter algorithm is a Monte Carlo simulation method based on non-parametric to achieve recursive Bayesian estimation algorithm, which can effectively solve nonlinear and non-Gaussian state estimation problems. The tracking method based on color features is insensitive to the rotation and posture of the face in which the face can be tracked
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