Data‐driven pedestrian re‐identification based on hierarchical semantic representation. (17th December 2017)

• Record Type: Journal Article
• Authors: Cheng, Keyang; Xu, Fangjie; Tao, Fei; Qi, Man; Li, Maozhen
• Other Names: Barbosa Jorge G. guestEditor.; Jeannot Emmanuel guestEditor.; Li Maozhen guestEditor.
• Is Part Of: Concurrency and computation. Volume 30:Number 23(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-12-17
• Subjects: attribute learning -- CAEs -- deep learning -- pedestrian re‐identification; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4403
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Limited number of labeled data of surveillance video causes the training of supervised model for pedestrian re‐identification to be a difficult task. Besides, applications of pedestrian re‐identification in pedestrian retrieving and criminal tracking are limited because of the lack of semantic representation. In this paper, a data‐driven pedestrian re‐identification model based on hierarchical semantic representation is proposed, extracting essential features with unsupervised deep learning model and enhancing the semantic representation of features with hierarchical mid‐level 'attributes'. Firstly, CNNs, well‐trained with the training process of CAEs, is used to extract features of horizontal blocks segmented from unlabeled pedestrian images. Then, these features are input into corresponding attribute classifiers to judge whether the pedestrian has the attributes. Lastly, with a table of 'attributes‐classes mapping relations', final result can be calculated. Under the premise of improving the accuracy of attribute classifier, our qualitative results show its clear advantages over the CHUK02, VIPeR, and i‐LIDS data set. Our proposed method is proved to effectively solve the problem of dependency on labeled data and lack of semantic expression, and it also significantly outperforms the state‐of‐the‐art in terms of accuracy and semanteme.