Multi-type Features Network for Person Re-identification
WANG Peng1, SONG Xiaoning1, WU Xiaojun1, YU Dongjun2
1.School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122; 2.School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094
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.
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2020, Vol. 33 
