Moving scene object tracking method based on deep convolutional neural network
The effect of target tracking is not ideal when facing various complex tracking scenarios such as non-rigid deformation of target, frequent occlusion, clutter of target background and interference of similar objects. In this paper, the feature based on deep convolutional neural network is used for t...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024-01-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016823010815 |
| _version_ | 1797347822338572288 |
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| author | Long Liu Bing Lin Yong Yang |
| author_facet | Long Liu Bing Lin Yong Yang |
| author_sort | Long Liu |
| collection | DOAJ |
| description | The effect of target tracking is not ideal when facing various complex tracking scenarios such as non-rigid deformation of target, frequent occlusion, clutter of target background and interference of similar objects. In this paper, the feature based on deep convolutional neural network is used for target tracking in moving scenes, and a sliding window target segmentation method is proposed to study the impact of data normalization and data set expansion on the final result. In order to select more distinguishing features, principal component analysis is used to process the features of Deep Convolution Neural Network (DCNN), and the features of different network layers of DCNN are compared. The feature coding algorithm is studied, and the extracted DCNN features are encoded by Fisher Vectors algorithm, and compared with the locality-constrained linear encoding technique. Experiments show that the feature based on deep convolutional neural network in this paper can obtain higher accuracy than the traditional feature fusion method. According to the result analysis, the tracking accuracy of deep convolutional neural network algorithm is improved under the condition of illumination variation. In the case of local occlusion, the tracking accuracy is also improved. |
| first_indexed | 2024-03-08T11:54:29Z |
| format | Article |
| id | doaj.art-7f4a25c04ef94816a071a9ecdd691ae1 |
| institution | Directory Open Access Journal |
| issn | 1110-0168 |
| language | English |
| last_indexed | 2024-03-08T11:54:29Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj.art-7f4a25c04ef94816a071a9ecdd691ae12024-01-24T05:17:19ZengElsevierAlexandria Engineering Journal1110-01682024-01-0186592602Moving scene object tracking method based on deep convolutional neural networkLong Liu0Bing Lin1Yong Yang2School of Physical Education, Chongqing Preschool Education College, Chongqing 404047, ChinaSchool of Physical Education, Chongqing Preschool Education College, Chongqing 404047, ChinaSchool of Physical Education, Zhengzhou University, Zhengzhou 450000, China; Corresponding author.The effect of target tracking is not ideal when facing various complex tracking scenarios such as non-rigid deformation of target, frequent occlusion, clutter of target background and interference of similar objects. In this paper, the feature based on deep convolutional neural network is used for target tracking in moving scenes, and a sliding window target segmentation method is proposed to study the impact of data normalization and data set expansion on the final result. In order to select more distinguishing features, principal component analysis is used to process the features of Deep Convolution Neural Network (DCNN), and the features of different network layers of DCNN are compared. The feature coding algorithm is studied, and the extracted DCNN features are encoded by Fisher Vectors algorithm, and compared with the locality-constrained linear encoding technique. Experiments show that the feature based on deep convolutional neural network in this paper can obtain higher accuracy than the traditional feature fusion method. According to the result analysis, the tracking accuracy of deep convolutional neural network algorithm is improved under the condition of illumination variation. In the case of local occlusion, the tracking accuracy is also improved.http://www.sciencedirect.com/science/article/pii/S1110016823010815Target trackingMotion sceneDeep convolutional neural networkFeature coding |
| spellingShingle | Long Liu Bing Lin Yong Yang Moving scene object tracking method based on deep convolutional neural network Alexandria Engineering Journal Target tracking Motion scene Deep convolutional neural network Feature coding |
| title | Moving scene object tracking method based on deep convolutional neural network |
| title_full | Moving scene object tracking method based on deep convolutional neural network |
| title_fullStr | Moving scene object tracking method based on deep convolutional neural network |
| title_full_unstemmed | Moving scene object tracking method based on deep convolutional neural network |
| title_short | Moving scene object tracking method based on deep convolutional neural network |
| title_sort | moving scene object tracking method based on deep convolutional neural network |
| topic | Target tracking Motion scene Deep convolutional neural network Feature coding |
| url | http://www.sciencedirect.com/science/article/pii/S1110016823010815 |
| work_keys_str_mv | AT longliu movingsceneobjecttrackingmethodbasedondeepconvolutionalneuralnetwork AT binglin movingsceneobjecttrackingmethodbasedondeepconvolutionalneuralnetwork AT yongyang movingsceneobjecttrackingmethodbasedondeepconvolutionalneuralnetwork |