Deep partial person re-identification via attention model
Junyeong Kim, Chang D. Yoo · 2017
This paper considers a novel algorithm referred to as deep partial person re-identification (DPPR) for partial person re-identification where only a part of a person is observed and full body images are available for identification. The DPPR is based on an end-to-end deep model which make use of convolutional neural network (CNN), RoI Pooling layer and attention model. The RoI Pooling layer enables the extraction of feature vector corresponding to predefined part of input image. The attention model selects a subset of CNN feature vectors. For qualitative evaluation of proposed model, data from CUHK03 are randomly cropped in constructing p-CUHK03. Experimental results show that DPPR outperforms our baseline model on p-CUHK03.