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

作　　者：Lu Tian Shengjin Wang 

机构地区：[1]Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.

出　　处：《Tsinghua Science and Technology》2018年第2期145-156,共12页清华大学学报（自然科学版（英文版）

基　　金：supported by the National Natural Science Foundation of China (No. 61071135);the National Science and Technology Support Program (No. 2013BAK02B04)

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(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.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.

关 键 词：person re-identification BAG-OF-WORDS unsupervised learning feature fusion 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]