Re-ranking Person Re-identification using Attributes Learning

Person re-identification (Re-ID) across multi-camera views remains a very challenging task. In fact, the use of global convolutional neural network (CNN) features is not optimal since the appearance similarity of identities is overlooked. Hence, persons with similar global appearance (wearing in very similar clothes) may be confused and hard to distinguish. To address these shared challenges, some attribute labels (such as hat or bag) can improve the discriminative ability of Re-ID model by depicting person images with more detailed descriptions. Hence, we propose in this paper a new attributes recognition model, called ARN, based on a deep CNN model. Then, we propose a new approach based on attributes similarity and k-reciprocal re-ranking algorithm in order to enhance person Re-ID baseline performances. The proposed approach, called K-RNNA, is motivated first of all, by the potential of re-ranking methods to resort the ’baseline’ re-identification results from the initial retrieval. Second, attributes, as a mid-level semantic representation of the person’s local appearance, could also potentially resolve these challenges. In fact, learned semantic attributes such as gender, hair, accessories (hat, bags, backpack, etc.) are used as a supplement during the re-ranking phase in order to discriminate between identities with similar global appearance. The experimental study conducted on Market-1501, Duke-MTMC-reID and CUHK03 datasets confirms that when attributes are used as supplement in the verification phase, we improve considerably the person Re-ID performances and we reach competitive results in terms of mean average precision (mAP) and Rank-1.