Multi-scale Context Aggregation for Video-Based Person Re-Identification

For video-based person re-identification (Re-ID), effectively aggregating video features is the key to dealing with various complicated situations. Different from previous methods that first extracted spatial features and later aggregated temporal features, we propose a Multi-scale Context Aggregation (MSCA) method in this paper to simultaneously learn spatial-temporal features from videos. Specifically, we design an Attention-aided Feature Pyramid Network (AFPN), which can recurrently aggregate detail and semantic information of multi-scale feature maps from the CNN backbone. To enable the aggregation to focus on more salient regions in the video, we embed a particular Spatial-Channel Attention module (SCA) into each layer of the pyramid. To further enhance the feature representations with temporal information while extracting the spatial features, we design a Temporal Enhancement module (TEM), which can plug into each layer of the backbone network in a plug-and-play manner. Comprehensive experiments on three standard video-based person Re-ID benchmarks demonstrate that our method is competitive with most state-of-the-art methods.