Privacy-Oriented Successive Approximation Image Position Follower Processing
In this paper, we analyze the location-following processing of the image by successive approximation with the need for directed privacy. To solve the detection problem of moving the human body in the dynamic background, the motion target detection module integrates the two ideas of feature informati...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/6853809 |
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| _version_ | 1850160098698067968 |
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| author | Ying Miao Danyang Shao Zhimin Yan |
| author_facet | Ying Miao Danyang Shao Zhimin Yan |
| author_sort | Ying Miao |
| collection | DOAJ |
| description | In this paper, we analyze the location-following processing of the image by successive approximation with the need for directed privacy. To solve the detection problem of moving the human body in the dynamic background, the motion target detection module integrates the two ideas of feature information detection and human body model segmentation detection and combines the deep learning framework to complete the detection of the human body by detecting the feature points of key parts of the human body. The detection of human key points depends on the human pose estimation algorithm, so the research in this paper is based on the bottom-up model in the multiperson pose estimation method; firstly, all the human key points in the image are detected by feature extraction through the convolutional neural network, and then the accurate labelling of human key points is achieved by using the heat map and offset fusion optimization method in the feature point confidence map prediction, and finally, the human body detection results are obtained. In the study of the correlation algorithm, this paper combines the HOG feature extraction of the KCF algorithm and the scale filter of the DSST algorithm to form a fusion correlation filter based on the principle study of the MOSSE correlation filter. The algorithm solves the problems of lack of scale estimation of KCF algorithm and low real-time rate of DSST algorithm and improves the tracking accuracy while ensuring the real-time performance of the algorithm. |
| format | Article |
| id | doaj-art-aefcaa29d37d432183035f752d729a63 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-aefcaa29d37d432183035f752d729a632025-08-20T02:23:15ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/68538096853809Privacy-Oriented Successive Approximation Image Position Follower ProcessingYing Miao0Danyang Shao1Zhimin Yan2School of Economics and Management, Shenyang Aerospace University, Shenyang, Liaoning 110136, ChinaSLZY (Shenyang) Hi-Tech Co., Ltd., Shenfu Reform and Innovation Demonstration Zone, Shenyang, Liaoning 110172, ChinaSLZY (Shenyang) Hi-Tech Co., Ltd., Shenfu Reform and Innovation Demonstration Zone, Shenyang, Liaoning 110172, ChinaIn this paper, we analyze the location-following processing of the image by successive approximation with the need for directed privacy. To solve the detection problem of moving the human body in the dynamic background, the motion target detection module integrates the two ideas of feature information detection and human body model segmentation detection and combines the deep learning framework to complete the detection of the human body by detecting the feature points of key parts of the human body. The detection of human key points depends on the human pose estimation algorithm, so the research in this paper is based on the bottom-up model in the multiperson pose estimation method; firstly, all the human key points in the image are detected by feature extraction through the convolutional neural network, and then the accurate labelling of human key points is achieved by using the heat map and offset fusion optimization method in the feature point confidence map prediction, and finally, the human body detection results are obtained. In the study of the correlation algorithm, this paper combines the HOG feature extraction of the KCF algorithm and the scale filter of the DSST algorithm to form a fusion correlation filter based on the principle study of the MOSSE correlation filter. The algorithm solves the problems of lack of scale estimation of KCF algorithm and low real-time rate of DSST algorithm and improves the tracking accuracy while ensuring the real-time performance of the algorithm.http://dx.doi.org/10.1155/2021/6853809 |
| spellingShingle | Ying Miao Danyang Shao Zhimin Yan Privacy-Oriented Successive Approximation Image Position Follower Processing Complexity |
| title | Privacy-Oriented Successive Approximation Image Position Follower Processing |
| title_full | Privacy-Oriented Successive Approximation Image Position Follower Processing |
| title_fullStr | Privacy-Oriented Successive Approximation Image Position Follower Processing |
| title_full_unstemmed | Privacy-Oriented Successive Approximation Image Position Follower Processing |
| title_short | Privacy-Oriented Successive Approximation Image Position Follower Processing |
| title_sort | privacy oriented successive approximation image position follower processing |
| url | http://dx.doi.org/10.1155/2021/6853809 |
| work_keys_str_mv | AT yingmiao privacyorientedsuccessiveapproximationimagepositionfollowerprocessing AT danyangshao privacyorientedsuccessiveapproximationimagepositionfollowerprocessing AT zhiminyan privacyorientedsuccessiveapproximationimagepositionfollowerprocessing |