KMS Chongqing Institute of Green and Intelligent Technology, CAS
| Cross- Scenario Person Re-identification | |
Jia,Ruoran; Liu,Shuguang
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| 2020-11-01 | |
| 摘要 | Abstract Person Reid is a challenging task for two factors, first one is background interference, such as changes in light, weather, posture, and camera position. Second is domain adaptive capacity, such as model train by market1501 achieve the same performance on Duke dataset. To solve the above problems, we come up with adopt human semantic to remove clutter from unwanted background information, is naturally a better alternative compare with bounding box, we adopt Local Regions Representation to extra the image features, which can preeminently improve the representation of local feature and global feature. Our proposed CSReID integrates human semantic and Local Regions Representation in person re-identification and not need to train on the evaluation dataset can achieve state of the art cross-modal performance. |
| DOI | 10.1088/1742-6596/1684/1/012071 |
| 发表期刊 | Journal of Physics: Conference Series
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| ISSN | 1742-6588 |
| 卷号 | 1684期号:1 |
| WOS记录号 | IOP:JPCS_1684_1_012071 |
| 语种 | 英语 |
