Research on a Face Real-time Tracking Algorithm Based on Particle Filter Multi-Feature Fusion
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-st...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/19/5/1245 |
| _version_ | 1811299238887292928 |
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| author | Tao Wang Wen Wang Hui Liu Tianping Li |
| author_facet | Tao Wang Wen Wang Hui Liu Tianping Li |
| author_sort | Tao Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T06:32:15Z |
| format | Article |
| id | doaj.art-c19478f67b054aa99a532dcce761118b |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T06:32:15Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-c19478f67b054aa99a532dcce761118b2022-12-22T02:58:04ZengMDPI AGSensors1424-82202019-03-01195124510.3390/s19051245s19051245Research on a Face Real-time Tracking Algorithm Based on Particle Filter Multi-Feature FusionTao Wang0Wen Wang1Hui Liu2Tianping Li3Shandong Key Laboratory of Medical Physics and Image Processing, School of Physics and Electronics, Shandong Normal University, Jinan 250014, ChinaSchool of Physics and Electronics, Shandong Normal University, Jinan 250014, ChinaSchool of Computer Science & Technology, Shandong University of Finance and Economics, Jinan 250014, ChinaSchool of Physics and Electronics, Shandong Normal University, Jinan 250014, ChinaWith 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.http://www.mdpi.com/1424-8220/19/5/1245video face trackingparticle filter (PF)features fusionupdating modeltemplate drift |
| spellingShingle | Tao Wang Wen Wang Hui Liu Tianping Li Research on a Face Real-time Tracking Algorithm Based on Particle Filter Multi-Feature Fusion Sensors video face tracking particle filter (PF) features fusion updating model template drift |
| title | Research on a Face Real-time Tracking Algorithm Based on Particle Filter Multi-Feature Fusion |
| title_full | Research on a Face Real-time Tracking Algorithm Based on Particle Filter Multi-Feature Fusion |
| title_fullStr | Research on a Face Real-time Tracking Algorithm Based on Particle Filter Multi-Feature Fusion |
| title_full_unstemmed | Research on a Face Real-time Tracking Algorithm Based on Particle Filter Multi-Feature Fusion |
| title_short | Research on a Face Real-time Tracking Algorithm Based on Particle Filter Multi-Feature Fusion |
| title_sort | research on a face real time tracking algorithm based on particle filter multi feature fusion |
| topic | video face tracking particle filter (PF) features fusion updating model template drift |
| url | http://www.mdpi.com/1424-8220/19/5/1245 |
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