Abstract
Video person re-identification (video Re-ID) is a key technology applied to video surveillance and security. Typical person re-identification is designed to retrieve the correct match of the target image (query) from gallery images, while video Re-ID extends this to query from gallery videos. The main factors affecting the video Re-ID model are: (i) a high-quality frame-level feature extractor, and (ii) temporal modeling that combines frame-level features into a feature for retrieval. In this work, we use ShuffleNet V2-based lightweight algorithm for video Re-ID, which can meet the demand for practical application and solve the problem of high consumption for computing resources, and maintain high performance. At the same time, the lightweight space attention mechanism Spatial Group-wise Enhance (SGE) module is used to view the person in more detail, which makes the feature representation more compact and effectively improves the retrieval accuracy. Finally, we design an Online Difference Discrimination (ODD) module to measure the feature gap between video frames, and use this module to make different temporal modeling for different quality video sequences. Experiments on three datasets (i.e., iLIDS-VID, PRID2011 and MARS) show that our method is competitive with state-of-the-art methods.
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