Person re-identification based on attention clustering and long short-term memory network

Abstract

Local features are widely applied in person re-identification (ReID) because of the rich fine-grained information. However, there are two problems existing in the local feature methods: the local regions are not accurate and the contextual information between different regions is not considered. To solve these problems, we propose a person ReID method based on attention clustering and long short-term memory (LSTM) network. First, in the feature extraction stage, an attention mechanism is utilized to suppress the background noise and a clustering operation is performed to extract accurate local features of human body parts. Second, we propose to regard the human body parts from head to foot as a sequence and utilize LSTM to take into account the contextual information between human body parts. Through the above two strategies, accurate local features of human body parts with contextual information can be extracted. In addition, we introduce the vector approximation-file index for fast ReID. Experiments on three benchmark datasets demonstrate the effectiveness of our method.