Abstract
The attention mechanism is effective in person re-identification. However, the performance of the combined use of different types of attention mechanisms needs to be improved, such as spatial attention and self-attention. An improved convolutional block attention model(CBAM-PRO) is proposed, and then a multi-type features network(MTFN) is proposed. The features of different interested domains are extracted through the integration of CBAM-Pro and self-attention mechanism, and the local features with different granularities are introduced concurrently to perform person re-identification jointly. The validity and reliability of MTFN are verified by the experiments on the existing general benchmark datasets.
| Translated title of the contribution | Multi-type Features Network for Person Re-identification |
|---|---|
| Original language | Chinese (Traditional) |
| Pages (from-to) | 879-888 |
| Number of pages | 10 |
| Journal | Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence |
| Volume | 33 |
| Issue number | 10 |
| DOIs | |
| State | Published - Oct 2020 |
| Externally published | Yes |