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

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(i Bo W) 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 i Bo W 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 VIPe R, PRID450 S, and Market1501.