Human re-identification with global and local siamese convolution neural network
Human re-identification is an important task in surveillance system to determine whether the same human re-appears in multiple cameras with disjoint views. Mostly, appearance based approaches are used to perform human re-identification task because they are less constrained than biometric based appr...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Universitas Ahmad Dahlan
2017
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| Online Access: | http://eprints.utm.my/75636/1/KBLow_HumanRe-identificationwithGlobal.pdf |
| _version_ | 1796862377160867840 |
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| author | Low, K. B. Sheikh, U. U. |
| author_facet | Low, K. B. Sheikh, U. U. |
| author_sort | Low, K. B. |
| collection | ePrints |
| description | Human re-identification is an important task in surveillance system to determine whether the same human re-appears in multiple cameras with disjoint views. Mostly, appearance based approaches are used to perform human re-identification task because they are less constrained than biometric based approaches. Most of the research works apply hand-crafted feature extractors and then simple matching methods are used. However, designing a robust and stable feature requires expert knowledge and takes time to tune the features. In this paper, we propose a global and local structure of Siamese Convolution Neural Network which automatically extracts features from input images to perform human re-identification task. Besides, most of the current human re-identification tasks in single-shot approaches do not consider occlusion issue due to lack of tracking information. Therefore, we apply a decision fusion technique to combine global and local features for occlusion cases in single-shot approaches. |
| first_indexed | 2024-03-05T20:10:43Z |
| format | Article |
| id | utm.eprints-75636 |
| institution | Universiti Teknologi Malaysia - ePrints |
| language | English |
| last_indexed | 2024-03-05T20:10:43Z |
| publishDate | 2017 |
| publisher | Universitas Ahmad Dahlan |
| record_format | dspace |
| spelling | utm.eprints-756362018-04-27T01:39:15Z http://eprints.utm.my/75636/ Human re-identification with global and local siamese convolution neural network Low, K. B. Sheikh, U. U. TK Electrical engineering. Electronics Nuclear engineering Human re-identification is an important task in surveillance system to determine whether the same human re-appears in multiple cameras with disjoint views. Mostly, appearance based approaches are used to perform human re-identification task because they are less constrained than biometric based approaches. Most of the research works apply hand-crafted feature extractors and then simple matching methods are used. However, designing a robust and stable feature requires expert knowledge and takes time to tune the features. In this paper, we propose a global and local structure of Siamese Convolution Neural Network which automatically extracts features from input images to perform human re-identification task. Besides, most of the current human re-identification tasks in single-shot approaches do not consider occlusion issue due to lack of tracking information. Therefore, we apply a decision fusion technique to combine global and local features for occlusion cases in single-shot approaches. Universitas Ahmad Dahlan 2017 Article PeerReviewed application/pdf en http://eprints.utm.my/75636/1/KBLow_HumanRe-identificationwithGlobal.pdf Low, K. B. and Sheikh, U. U. (2017) Human re-identification with global and local siamese convolution neural network. Telkomnika (Telecommunication Computing Electronics and Control), 15 (2). pp. 726-732. ISSN 1693-6930 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020178250&doi=10.12928%2fTELKOMNIKA.v15i2.6121&partnerID=40&md5=0e48ee9c286279745910c7cede51fc58 |
| spellingShingle | TK Electrical engineering. Electronics Nuclear engineering Low, K. B. Sheikh, U. U. Human re-identification with global and local siamese convolution neural network |
| title | Human re-identification with global and local siamese convolution neural network |
| title_full | Human re-identification with global and local siamese convolution neural network |
| title_fullStr | Human re-identification with global and local siamese convolution neural network |
| title_full_unstemmed | Human re-identification with global and local siamese convolution neural network |
| title_short | Human re-identification with global and local siamese convolution neural network |
| title_sort | human re identification with global and local siamese convolution neural network |
| topic | TK Electrical engineering. Electronics Nuclear engineering |
| url | http://eprints.utm.my/75636/1/KBLow_HumanRe-identificationwithGlobal.pdf |
| work_keys_str_mv | AT lowkb humanreidentificationwithglobalandlocalsiameseconvolutionneuralnetwork AT sheikhuu humanreidentificationwithglobalandlocalsiameseconvolutionneuralnetwork |