Improved Bag-of-Words Model for Person Re-identification

摘要

Person re-identification (person re-id) aims to match observations on pedestrians from different cameras.It is a challenging task in real word surveillance systems and draws extensive attention from the community.Most existing methods are based on supervised learning which requires a large number of labeled data.In this paper,we develop a robust unsupervised learning approach for person re-id.We propose an improved Bag-of-Words (iBoW) model to describe and match pedestrians under different camera views.The proposed descriptor does not require any re-id labels,and is robust against pedestrian variations.Experiments show the proposed iBoW descriptor outperforms other unsupervised methods.By combination with efficient metric learning algorithms,we obtained competitive accuracy compared to existing state-of-the-art methods on person re-identification benchmarks,including VIPeR,PRID450S,and Market1501.