Data-driven and semantic-based pedestrian re-identification

Abstract

Pedestrian Re-identification faces many difficulties in training of supervised model because of limited number of labeled data of surveillance videos. 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, this model 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. Our method is proved to significantly outperform the state of the art on the VIPeR and i-LIDS data set in the aspects of accuracy and semanteme.