  Extracting effective and discriminative features is very important for
addressing the challenging person re-identification (re-ID) task. Prevailing
deep convolutional neural networks (CNNs) usually use high-level features for
identifying pedestrian. However, some essential spatial information resided in
low-level features such as shape, texture and color will be lost when learning
the high-level features, due to extensive padding and pooling operations in the
training stage. In addition, most existing person re-ID methods are mainly
based on hand-craft bounding boxes where images are precisely aligned. It is
unrealistic in practical applications, since the exploited object detection
algorithms often produce inaccurate bounding boxes. This will inevitably
degrade the performance of existing algorithms. To address these problems, we
put forward a novel person re-ID model that fuses high- and low-level
embeddings to reduce the information loss caused in learning high-level
features. Then we divide the fused embedding into several parts and reconnect
them to obtain the global feature and more significant local features, so as to
alleviate the affect caused by the inaccurate bounding boxes. In addition, we
also introduce the spatial and channel attention mechanisms in our model, which
aims to mine more discriminative features related to the target. Finally, we
reconstruct the feature extractor to ensure that our model can obtain more
richer and robust features. Extensive experiments display the superiority of
our approach compared with existing approaches. Our code is available at
https://github.com/libraflower/MutipleFeature-for-PRID.
