Video face target detection and tracking algorithm based on nonlinear sequence Monte Carlo filtering technique
In order to achieve facial object detection and tracking in video, a method based on nonlinear sequence Monte Carlo filtering technology is proposed. The algorithm is simple, effective, and easy to operate, which can solve the problems of scale change and occlusion in the process of online learning...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2023-10-01
|
| Series: | Nonlinear Engineering |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/nleng-2022-0329 |
| _version_ | 1827780122782990336 |
|---|---|
| author | Du Yunming Liu Yi Tian Jing |
| author_facet | Du Yunming Liu Yi Tian Jing |
| author_sort | Du Yunming |
| collection | DOAJ |
| description | In order to achieve facial object detection and tracking in video, a method based on nonlinear sequence Monte Carlo filtering technology is proposed. The algorithm is simple, effective, and easy to operate, which can solve the problems of scale change and occlusion in the process of online learning tracking, so as to ensure the smooth implementation of learning effect evaluation. Experimental methods should be added to the article summary section. The results show that the algorithm in this study outperforms the basic KCF in terms of evaluation accuracy and success rate, as well as outperforms other tracker algorithms in benchmark, achieving scores of 0.837 and 0.705, respectively. In terms of overlapping accuracy, the reason why this study’s algorithm is higher than KCF is that this study determines the tracking status of the current target by calculating the primary side regulated (PSR) value when the target is obscured or lost, which does not make the tracking error to accumulate. The tracking algorithm in this study is not ranked first in the two attributes of motion blur and low resolution, but the rankings of all other nine attributes belong to the first. Compared with the KCF algorithm, the accuracy plots for the three attributes of scale change, occlusion, and leaving the field of view are improved by 10.26, 13.48, and 13.04%, respectively. Thus, it is proved that the method based on nonlinear sequence Monte Carlo filtering technology can achieve video facial object detection and tracking. |
| first_indexed | 2024-03-11T15:00:16Z |
| format | Article |
| id | doaj.art-e51d553d99df42309e01fcae1f2dcc72 |
| institution | Directory Open Access Journal |
| issn | 2192-8029 |
| language | English |
| last_indexed | 2024-03-11T15:00:16Z |
| publishDate | 2023-10-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Nonlinear Engineering |
| spelling | doaj.art-e51d553d99df42309e01fcae1f2dcc722023-10-30T07:58:46ZengDe GruyterNonlinear Engineering2192-80292023-10-01121370223810.1515/nleng-2022-0329Video face target detection and tracking algorithm based on nonlinear sequence Monte Carlo filtering techniqueDu Yunming0Liu Yi1Tian Jing2College of Information Science and Electronic Technology, Jiamusi University, Jiamusi154007, ChinaCollege of Information Science and Electronic Technology, Jiamusi University, Jiamusi154007, ChinaCollege of Information Science and Electronic Technology, Jiamusi University, Jiamusi154007, ChinaIn order to achieve facial object detection and tracking in video, a method based on nonlinear sequence Monte Carlo filtering technology is proposed. The algorithm is simple, effective, and easy to operate, which can solve the problems of scale change and occlusion in the process of online learning tracking, so as to ensure the smooth implementation of learning effect evaluation. Experimental methods should be added to the article summary section. The results show that the algorithm in this study outperforms the basic KCF in terms of evaluation accuracy and success rate, as well as outperforms other tracker algorithms in benchmark, achieving scores of 0.837 and 0.705, respectively. In terms of overlapping accuracy, the reason why this study’s algorithm is higher than KCF is that this study determines the tracking status of the current target by calculating the primary side regulated (PSR) value when the target is obscured or lost, which does not make the tracking error to accumulate. The tracking algorithm in this study is not ranked first in the two attributes of motion blur and low resolution, but the rankings of all other nine attributes belong to the first. Compared with the KCF algorithm, the accuracy plots for the three attributes of scale change, occlusion, and leaving the field of view are improved by 10.26, 13.48, and 13.04%, respectively. Thus, it is proved that the method based on nonlinear sequence Monte Carlo filtering technology can achieve video facial object detection and tracking.https://doi.org/10.1515/nleng-2022-0329face trackingexpression recognitiondeep learningsequential monte carlo filtering |
| spellingShingle | Du Yunming Liu Yi Tian Jing Video face target detection and tracking algorithm based on nonlinear sequence Monte Carlo filtering technique Nonlinear Engineering face tracking expression recognition deep learning sequential monte carlo filtering |
| title | Video face target detection and tracking algorithm based on nonlinear sequence Monte Carlo filtering technique |
| title_full | Video face target detection and tracking algorithm based on nonlinear sequence Monte Carlo filtering technique |
| title_fullStr | Video face target detection and tracking algorithm based on nonlinear sequence Monte Carlo filtering technique |
| title_full_unstemmed | Video face target detection and tracking algorithm based on nonlinear sequence Monte Carlo filtering technique |
| title_short | Video face target detection and tracking algorithm based on nonlinear sequence Monte Carlo filtering technique |
| title_sort | video face target detection and tracking algorithm based on nonlinear sequence monte carlo filtering technique |
| topic | face tracking expression recognition deep learning sequential monte carlo filtering |
| url | https://doi.org/10.1515/nleng-2022-0329 |
| work_keys_str_mv | AT duyunming videofacetargetdetectionandtrackingalgorithmbasedonnonlinearsequencemontecarlofilteringtechnique AT liuyi videofacetargetdetectionandtrackingalgorithmbasedonnonlinearsequencemontecarlofilteringtechnique AT tianjing videofacetargetdetectionandtrackingalgorithmbasedonnonlinearsequencemontecarlofilteringtechnique |