Deep Learning-based Person Search with Visual Attention Embedding

In this work, we consider the problem of person search, which is a challenging task that requires both person detection and person re-identification r un c oncurrently. In this context, we propose a person search approach based on deep neural networks that incorporates attention mechanisms to perform retrieval more robustly. Global and local features are extracted for person detection and person identification, respectively, boosted by attention layers that allow the extraction of discriminative feature representations, all in an end-to-end manner. We evaluate our approach on three challenging data sets and show that our proposed method improves the state-of-the-art networks.