Modeling the underlying person structure for person re-identification (re-ID) is difficult due to diverse deformable poses, changeable camera views and imperfect person detectors. How to exploit underlying person structure information without extra annotations to improve the performance of person re-ID remains largely unexplored. To address this problem, we propose a novel Relative Local Distance (RLD) method that integrates a relative local distance constraint into convolutional neural networks (CNNs) in an end-to-end way. It is the first time that the relative local constraint is proposed to guide the global feature representation learning. Specially, a relative local distance matrix is computed by using feature maps and then regarded as a regularizer to guide CNNs to learn a structure-aware feature representation. With the discovered underlying person structure, the RLD method builds a bridge between the global and local feature representation and thus improves the capacity of feature representation for person re-ID. Furthermore, RLD also significantly accelerates deep network training compared with conventional methods. The experimental results show the effectiveness of RLD on the CUHK03, Market-1501, and DukeMTMC-reID datasets. Code is available at this https URL .
Differently, some studies @cite_19 @cite_27 @cite_6 attempted to conduct local metric learning to align the person structure. For example, Shen et. al. @cite_19 introduced a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair. In @cite_27 , a patch-based deformable model is proposed to combine appearance term with a deformation cost that controls relative placement of patches. Li et. al. @cite_6 proposed a filter pairing neural network to handle misalignment and geometric transforms by integrating a patch matching layer into the neural network. Our RLD method differs a lot from these patch-based matching methods in that RLD focuses on self-structure exploration while patch-based matching methods focus on the matching between image pairs. Patch-based matching methods are not off-line algorithms and cannot extract gallery features in advance. When the gallery image set is very large, it costs too much time to extract deep features for person retrieval.
@cite_19 @cite_27 @cite_6
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This paper addresses the problem of handling spatial misalignments due to camera-view changes or human-pose variations in person re-identification. We first introduce a boosting-based approach to learn a correspondence structure which indicates the patch-wise matching probabilities between images from a target camera pair. The learned correspondence structure can not only capture the spatial correspondence pattern between cameras but also handle the viewpoint or human-pose variation in individual images. We further introduce a global-based matching process. It integrates a global matching constraint over the learned correspondence structure to exclude cross-view misalignments during the image patch matching process, hence achieving a more reliable matching score between images. Experimental results on various datasets demonstrate the effectiveness of our approach. The methodology for finding the same individual in a network of cameras must deal with significant changes in appearance caused by variations in illumination, viewing angle, and a person’s pose. Re-identification requires solving two fundamental problems: 1) determining a distance measure between features extracted from different cameras that cope with illumination changes (metric learning) and 2) ensuring that matched features refer to the same body part (correspondence). Most metric learning approaches focus on finding a robust distance measure between bounding box images, neglecting the alignment aspects. In this paper, we propose to learn appearance measures for patches that are combined using deformable models. Learning metrics for patches avoids strong dimensionality reduction, thus keeping more information. Additionally, we allow patches to change their locations, directly addressing the correspondence problem. As patches from different locations may share the same metric, our method effectively multiplies the amount of training data and allows patch metrics to be learned on the smaller amounts of labeled images. Different metric learning approaches (KISSME, XQDA, and LSSL) together with different deformable models (spring constraints and one-to-one matching constraints) are investigated and compared. For describing patches, we propose to learn a deep feature representation with convolutional neural networks, thus obtaining highly effective features for re-identification. We demonstrate that our approach significantly outperforms state-of-the-art methods on multiple data sets. In this paper, we address the person re-identification problem, discovering the correct matches for a probe person image from a set of gallery person images. We follow the learning-to-rank methodology and learn a similarity function to maximize the difference between the similarity scores of matched and unmatched images for a same person. We introduce at least three contributions to person re-identification. First, we present an explicit polynomial kernel feature map, which is capable of characterizing the similarity information of all pairs of patches between two images, called soft-patch-matching, instead of greedily keeping only the best matched patch, and thus more robust. Second, we introduce a mixture of linear similarity functions that is able to discover different soft-patch-matching patterns. Last, we introduce a negative semi-definite regularization over a subset of the weights in the similarity function, which is motivated by the connection between explicit polynomial kernel feature map and the Mahalanobis distance, as well as the sparsity constraint over the parameters to avoid over-fitting. Experimental results over three public benchmarks demonstrate the superiority of our approach. Human eyes can recognize person identities based on small salient regions, i.e., person saliency is distinctive and reliable in pedestrian matching across disjoint camera views. However, such valuable information is often hidden when computing similarities of pedestrian images with existing approaches. Inspired by our user study result of human perception on person saliency, we propose a novel perspective for person re-identification based on learning person saliency and matching saliency distribution. The proposed saliency learning and matching framework consists of four steps: (1) To handle misalignment caused by drastic viewpoint change and pose variations, we apply adjacency constrained patch matching to build dense correspondence between image pairs. (2) We propose two alternative methods, i.e., K-Nearest Neighbors and One-class SVM, to estimate a saliency score for each image patch, through which distinctive features stand out without using identity labels in the training procedure. (3) saliency matching is proposed based on patch matching. Matching patches with inconsistent saliency brings penalty, and images of the same identity are recognized by minimizing the saliency matching cost. (4) Furthermore, saliency matching is tightly integrated with patch matching in a unified structural RankSVM learning framework. The effectiveness of our approach is validated on the four public datasets. Our approach outperforms the state-of-the-art person re-identification methods on all these datasets.