| Autor: |
Ali Akbari, Muhammad Awais, Josef Kittler, Ammarah Farooq, Syed Safwan Khalid |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
IJCB |
| DOI: |
10.1109/ijcb48548.2020.9304940 |
| Popis: |
Classical person re-identification approaches assume that a person of interest has appeared across different cameras and can be queried by one of the existing images. However, in real-world surveillance scenarios, frequently no visual information will be available about the queried person. In such scenarios, a natural language description of the person by a witness will provide the only source of information for retrieval. In this work, person re-identification using both vision and language information is addressed under all possible gallery and query scenarios. A two stream deep convolutional neural network framework supervised by identity based cross entropy loss is presented. Canonical Correlation Analysis is performed to enhance the correlation between the two modalities in a joint latent embedding space. To investigate the benefits of the proposed approach, a new testing protocol under a multi modal ReID setting is proposed for the test split of the CUHK-PEDES and CUHK-SYSU benchmarks. The experimental results verify that the learnt visual representations are more robust and perform 20% better during retrieval as compared to a single modality system. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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