Person re-identification with activity prediction based on hierarchical spatial-temporal model
| Autor: | Minxian Li, Chao Guan, Jinhui Tang, Fumin Shen, Jingya Wang |
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| Rok vydání: | 2018 |
| Předmět: |
Recall
Computer science business.industry Cognitive Neuroscience 020206 networking & telecommunications 02 engineering and technology computer.software_genre Machine learning Re identification Computer Science Applications Range (mathematics) Artificial Intelligence Path (graph theory) 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence Data mining business Focus (optics) computer |
| Zdroj: | Neurocomputing. 275:1200-1207 |
| ISSN: | 0925-2312 |
| DOI: | 10.1016/j.neucom.2017.09.064 |
| Popis: | Person re-identification (re-id) across cameras remains a very challenging problem, especially when the wide range searching exists in a multi-camera surveillance network. Current person re-identification methods focus on using visual model to search the specified person. In fact, in practical applications, due to the large-scale search range, the searching way only relying on visual model is not efficient. Moreover, the recall ability of visual model usually is limited in large-scale searching, because it does not consider the spatial-temporal information of person. However, the current public re-id datasets only include the visual samples. To address this problem, in this work, we collect a large-scale re-id dataset, PKU-SVD-B-REID, which includes both visual and spatial-temporal information of over 133 K samples. Then, we propose a novel person re-id framework, named Hierarchical Spatial-Temporal Model (HSTM), which can effectively predict the person activity path and reduce the search range in the real multiple cameras surveillance system. Extensive experiments on PKU-SVD-B-REID validate the superiority of our method over conventional re-id methods based on only visual information in terms of both efficiency and accuracy. |
| Databáze: | OpenAIRE |
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