Multi-scale Spatial-temporal Network for Person Re-identification

Video-based person re-identification (ReID) is an important task, which has received much attention in recent years due to its efficiency in the field of surveillance. Researchers have employed many effective approaches for video-based person ReID, but there are still two problems. Firstly, the same pedestrian in the video sequences differs in size. Secondly, traditional RNNs can only process one-dimension features, which are not suitable for dealing with video sequences. To solve above problems, we propose a new network called Multi-scale Spatial-Temporal Network (MSTN), which combines multi-scale feature extractor and CLSTM together to tackle the discrepant sizes of pedestrians and extract more representative temporal information for the video sequences. We conduct the experiments on the iLIDS-VID, PRID-2011 and MARS datasets, and our approach outperforms state-of-the-art methods by a large margin.