Person re-identification (re-ID) has been a popular topic in computer vision and pattern recognition communities for a decade. Several important milestones such as metric-based and deeply-learned re-ID in recent years have promoted this topic. However, most existing re-ID works are designed for closed-world scenarios rather than realistic open-world settings, which limits the practical application of the re-ID technique. On one hand, the performance of the latest re-ID methods has surpassed the human-level performance on several commonly used benchmarks (e.g., Market1501 and CUHK03), which are collected from closed-world scenarios. On the other hand, open-world tasks that are less developed and more challenging have received increasing attention in the re-ID community. Therefore, this paper starts the first attempt to analyze the trends of open-world re-ID and summarizes them from both narrow and generalized perspectives. In the narrow perspective, open-world re-ID is regarded as person verification (i.e., open-set re-ID) instead of person identification, that is, the query person may not occur in the gallery set. In the generalized perspective, application-driven methods that are designed for specific applications are defined as generalized open-world re-ID. Their settings are usually close to realistic application requirements. Specifically, this survey mainly includes the following four points for open-world re-ID: 1) analyzing the discrepancies between closed- and open-world scenarios; 2) describing the developments of existing open-set re-ID works and their limitations; 3) introducing specific application-driven works from three aspects, namely, raw data, practical procedure, and efficiency; and 4) summarizing the state-of-the-art methods and future directions for open-world re-ID. This survey on open-world re-ID provides a guidance for improving the usability of re-ID technique in practical applications.

The number of superpixels (i.e., segmentation scale) is crucial for spectral-spatial hyperspectral image (HSI) classification. Existing methods always set the segmentation scale through a manually experimental strategy, which is time-consuming and unsuitable for various complicated practical applications. The information fusion of complementary multiple scales is proven to be more effective than the single scale for HSI classification, but the scale level is still set manually. In this article, we propose a novel adaptive multiscale segmentations (AMSs) method that can automatically provide a set of suitable scales that are adapted to different hyperspectral data. Specifically, based on the assumption that the segmentation scale of HSI is related to the image complexity itself, the texture ratio and the number of land cover classes are used to examine a candidate scale pool. A good scale means that it contains a small spectral difference between pixels within the same superpixel (intrasuperpixel discrimination index) and a large discrepancy between neighboring superpixels (intersuperpixel discrimination index). Thus, an intra-interscale discrimination index is defined and applied to depict the characteristics of the scale. Then, the scale with the best intra-inter discrimination index, which usually has satisfactory performance, is treated as the initially selected scale. The remaining suitable scales are iteratively compared with the selected ones and then added to the target scale pool, until the newly added scale can no longer provide significantly complementary information. Extensive experimental results on three HSI data sets have demonstrated the effectiveness of the proposed AMS when compared with state-of-the-art methods.

Visible thermal person re-identification (VT-ReID) is a challenging cross-modality pedestrian retrieval problem due to the large intra-class variations and modality discrepancy across different cameras. Existing VT-ReID methods mainly focus on learning cross-modality sharable feature representations by handling the modality-discrepancy in feature level. However, the modality difference in classifier level has received much less attention, resulting in limited discriminability. In this paper, we propose a novel modality-aware collaborative ensemble (MACE) learning method with middle-level sharable two-stream network (MSTN) for VT-ReID, which handles the modality-discrepancy in both feature level and classifier level. In feature level, MSTN achieves much better performance than existing methods by capturing sharable discriminative middlelevel features in convolutional layers. In classifier level, we introduce both modality-specific and modality-sharable identity classifiers for two modalities to handle the modality discrepancy. To utilize the complementary information among different classifiers, we propose an ensemble learning scheme to incorporate the modality sharable classifier and the modality specific classifiers. In addition, we introduce a collaborative learning strategy, which regularizes modality-specific identity predictions and the ensemble outputs. Extensive experiments on two cross-modality datasets demonstrate that the proposed method outperforms current state-of-the-art by a large margin, achieving rank- 1/mAP accuracy 51.64%/50.11% on the SYSU-MM01 dataset, and 72.37%/69.09% on the RegDB dataset.

Visible thermal person re-identification (VT-ReID) is a cross-modality pedestrian retrieval problem, which automatically searches persons between day-time visible images and night-time thermal images. Despite the extensive progress in single-modality ReID, the cross-modality pedestrian retrieval problem has limited attention due to its challenges in modality discrepancy and large intra-class variations across cameras. Existing cross-modality ReID methods usually solve this problem by learning cross-modality feature representations with modality-sharable classifier. However, this learning strategy may lose discriminative information in different modalities. In this paper, we propose a novel modality-aware collaborative (MAC) learning method on top of a two-stream network for VT-ReID, which handles the modality-discrepancy in both feature level and classifier level. In feature level, it handles the modality discrepancy by a two-stream network with different parameters. In classifier level, it contains two separate modality-specific identity classifiers for two modalities to capture the modality-specific information, and they have the same network architecture but different parameters. In addition, we introduce a collaborative learning scheme, which regularizes the modality-sharable and modality-specific identity classifiers by utilizing the relationship between different classifiers. Extensive experiments on two cross-modality person re-identification datasets demonstrate the superiority of the proposed method, achieving much better performance than the state-of-the-art.

Person re-identification, aiming to identify images of the same person from various cameras configured in different places, has attracted much attention in the multimedia retrieval community. In this problem, choosing a proper distance metric is a crucial aspect, and many classic methods utilize a uniform learnt metric. However, their performance is limited due to ignoring the zero-shot and fine-grained characteristics presented in real person re-identification applications. In this paper, we investigate two consistencies across two cameras, which are cross-view support consistency and cross-view projection consistency. The philosophy behind it is that, in spite of visual changes in two images of the same person under two camera views, the support sets in their respective views are highly consistent, and after being projected to the same view, their context sets are also highly consistent. Based on the above phenomena, we propose a data-driven distance metric (DDDM) method, re-exploiting the training data to adjust the metric for each query-gallery pair. Experiments conducted on three public data sets have validated the effectiveness of the proposed method, with a significant improvement over three baseline metric learning methods. In particular, on the public VIPeR dataset, the proposed method achieves an accuracy rate of 42.09% at rank-1, which outperforms the state-of-the-art methods by 4.29%.

Person reidentification is a key technique to match different persons observed in nonoverlapping camera views. Many researchers treat it as a special object-retrieval problem, where ranking optimization plays an important role. Existing ranking optimization methods mainly utilize the similarity relationship between the probe and gallery images to optimize the original ranking list, but seldom consider the important dissimilarity relationship. In this paper, we propose to use both similarity and dissimilarity cues in a ranking optimization framework for person reidentification. Its core idea is that the true match should not only be similar to those strongly similar galleries of the probe, but also be dissimilar to those strongly dissimilar galleries of the probe. Furthermore, motivated by the philosophy of multiview verification, a ranking aggregation algorithm is proposed to enhance the detection of similarity and dissimilarity based on the following assumption: the true match should be similar to the probe in different baseline methods. In other words, if a gallery blue image is strongly similar to the probe in one method, while simultaneously strongly dissimilar to the probe in another method, it will probably be a wrong match of the probe. Extensive experiments conducted on public benchmark datasets and comparisons with different baseline methods have shown the great superiority of the proposed ranking optimization method.

Searching for specific persons from surveillance videos captured by different cameras, is a key yet under-addressed challenge in multimedia system. Related person retrieval works mainly focus on searching person by visual appearance, known as person re-identification. However, the initial visual image may not be available in some practical applications. For example, the criminal is described by a text description indirectly, "A young woman wearing a red casual with a backpack", the traditional methods can not conquer this issue. Based on a set of pre-defined attributes that the text description query can be transformed to an attribute vector, thus can be used to retrieval in the gallery set. And yet, the user-provided attributes are sometimes incomplete. This new issue is defined as Specific Person Retrieval via Incomplete Text Description. In this paper, we conduct a specific attribute completion to enrich the original text query and generate a more expressive attribute vector. Then, a pairwise-based metric learning is introduced for completed attribute vectors. Extensive experiments conducted on two benchmark datasets have shown our superior performance.

Person re-identification is a key technique to match different persons observed in non-overlapping camera views.Many researchers treat it as a special object retrieval problem, where ranking optimization plays an important role. Existing ranking optimization methods utilize the similarity relationship between the probe and gallery images to optimize the original ranking list in which dissimilarity relationship is seldomly investigated. In this paper, we propose to use both similarity and dissimilarity cues in a ranking optimization framework for person re-identification. Its core idea is based on the phenomenon that the true match should not only be similar to the strong similar samples of the probe but also dissimilar to the strong dissimilar samples. Extensive experiments have shown the great superiority of the proposed ranking optimization method.

Matching individuals within a group of spatially nonoverlapping surveillance cameras, also known as person reidentification, has recently attracted a lot of research interest. Current methods mainly focus on feature representation or distance measure, which directly compare person images captured by different cameras. However, it is still a problem because of various surveillance conditions; for example, view switching, lighting variations, and image scaling. Although the brightness transfer function was proposed to address the problem of illumination variation, it could not handle view and scale changes among various cameras. In this paper, we propose a new approach to compensate for the inconsistency of feature distributions of person images captured by different cameras. More precisely, a feature projection matrix (FPM) is learned to project image features of one camera to the feature space of another camera, from which the latent device difference can be effectively eliminated for the person reidentification task. In particular, we formulate the FPM learning as a smooth unconstrained convex optimization problem and use a simple gradient descent algorithm with stochastic samples to accelerate the solving process. Extensive comparative experiments conducted on three standard datasets have shown the promising prospect of the proposed method.

This paper proposes a simple but efficient bidirectional ranking method to improve person re-identification results across non-overlapping cameras. Previous methods treat person reidentification as a special object retrieval problem, and compute the final rank result purely based on a unidirectional matching between the probe and all gallery images. However, the expected person image may be excluded from the probe's ??-nearest neighbor due to appearance changes caused by variations in illuminations, poses, viewpoints and occlusion. To solve the above problem, our method queries every gallery image in a new gallery composed of the original probe image and other gallery images, and revises the initial query result in accordance with both content and context similarities between bidirectional ranking lists. A latent assumption of our method is that images of the same person should not only have similar visual content, known as content similarity, but also possess similar k-nearest neighbors, known as context similarity. Extensive experiments conducted on a series of standard data sets have validated the effectiveness of our proposed method with an average improvement of 5-10% over original baseline methods.

Matching individuals across a group of spatially non-overlapping surveillance cameras, also known as person re-identification, has recently attracted a lot of research interests. Current methods mainly focus on feature extraction or metric learning, which directly compare person images captured by different cameras, but seldom consider device differences caused by various surveillance conditions, e.g. view switching, scale zooming and illumination variation. Although brightness transfer function was proposed to address the problem of illumination variation, it could not handle view and scale changes among various cameras. In this paper, we propose an effective data-driven method to conquer device differences in the practical surveillance camera network. More precisely, with the help of a set of labelled pair-wise person images captured by two disjoint cameras, a feature projection matrix can be learned to project the person images of one camera to the feature space of the other camera, and thus images from these two different cameras can be accurately compared in a common feature space. Extensive comparative experiments conducted on three standard datasets have shown the promising prospect of our proposed methods.