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Person search is a time-consuming computer vision task that entails locating and recognizing query people in scenic pictures. Body components are commonly mismatched during matching due to position variation, occlusions, and partially absent body parts, resulting in unsatisfactory person search results. Existing approaches for extracting local characteristics of the human body using keypoint information are unable to handle the search job when distinct body parts are misaligned, ignoring to exploit multiple granularities, which is crucial in the person search process. Moreover, the alignment learning methods learn body part features with fixed and equal weights, ignoring the beneficial contextual information, e.g., the umbrella carried by the pedestrian, which supplements compelling clues for identifying the person. In this paper, we propose a Coarse-to-Fine Adaptive Alignment Representation (CFA2R) network for learning multiple granular features in misaligned person search in the coarse-to-fine perspective. To exploit more beneficial body parts and related context of the cropped pedestrians, we design a Part-Attentional Progressive Module (PAPM) to guide the network to focus on informative body parts and positive accessorial regions. Besides, we propose a Re-weighting Alignment Module (RAM) shedding light on more contributive parts instead of treating them equally. Specifically, adaptive re-weighted but not fixed part features are reconstructed by Re-weighting Reconstruction module, considering that different parts serve unequally during image matching. Extensive experiments conducted on CUHK-SYSU and PRW datasets demonstrate competitive performance of our proposed method. [ABSTRACT FROM AUTHOR]

Recently, advances in person re-identification (Re-ID) has benefitted from use of the popular multi-branch network. However, performing feature learning in a single branch with uniform partitioning is likely to separate meaningful local regions, and correlation among different branches is not well established. In this article, we propose a novel harmonious multi-branch network (HMBN) to relieve these intra-branch and inter-branch problems harmoniously. HMBN is a multi-branch network with various stripes on different branches to learn coarse-to-fine pedestrian information. We first replace the uniform partition with a horizontal overlapped partition to cover meaningful local regions between adjacent stripes in a single branch. We then incorporate a novel attention module to make all branches interact by modeling spatial contextual dependencies across branches. Finally, in order to train the HMBN more effectively, a harder triplet loss is introduced to optimize triplets in a harder manner. Extensive experiments are conducted on three benchmark datasets -- DukeMTMC-reID, CUHK03, and Market-1501 -- demonstrating the superiority of our proposed HMBN over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Person re-identification (Re-ID) is a challenging and arduous task due to non-overlapping views, complex background, and uncontrollable occlusion in video surveillance. An existing method for capturing pedestrian local region information is to divide person regions into horizontal stripes, which may lead to invalid features and erroneous learning. To solve this problem, this paper proposes a 3D skeleton and a two-stream approach to person Re-ID. The first stream of the method uses the 3D skeleton for background filtering and region segmentation. The second stream uses Siamese net to extract the global descriptor. The features of the two streams are fused to preserve the integrity of the person. An optimized region matching method for metric learning is designed. Extensive comparing experiments were conducted with state-of-the-art Re-ID methods on the Market-1501, CUHK03, and DukeMTMC-reID datasets. Experimental results show that the proposed method outperforms the existing methods in recognition accuracy. [ABSTRACT FROM AUTHOR]

To cope with the problem caused by inadequate training data, many person re-identification (re-id) methods exploit generative adversarial networks (GAN) for data augmentation, where the training of GAN is typically independent of that of the re-id model. The coupling relation between them that probably brings in a performance gain of re-id is thus ignored. In this work, we propose a general framework, namely JoT-GAN, to jointly train GAN and the re-id model. It can simultaneously achieve the optima of both the generator and the re-id model, where the training is guided by each other through a discriminator. The re-id model is boosted for two reasons: (1) the adversarial training encourages it to fool the discriminator, and (2) the generated samples augment the training data. Extensive results on benchmark datasets show that for the re-id model trained with the identification loss as well as the triplet loss, the proposed joint training framework outperforms existing methods with separate training and achieves state-of-the-art re-id performance. [ABSTRACT FROM AUTHOR]

In this article, we observe that most false positive images (i.e., different identities with query images) in the top ranking list usually have the similar color information with the query image in person re-identification (Re-ID). Meanwhile, when we use the greyscale images generated from RGB images to conduct the person Re-ID task, some hard query images can obtain better performance compared with using RGB images. Therefore, RGB and greyscale images seem to be complementary to each other for person Re-ID. In this article, we aim to utilize both RGB and greyscale images to improve the person Re-ID performance. To this end, we propose a novel two-stream deep neural network with RGB-grey information, which can effectively fuse RGB and greyscale feature representations to enhance the generalization ability of Re-ID. First, we convert RGB images to greyscale images in each training batch. Based on these RGB and greyscale images, we train the RGB and greyscale branches, respectively. Second, to build up connections between RGB and greyscale branches, we merge the RGB and greyscale branches into a new joint branch. Finally, we concatenate the features of all three branches as the final feature representation for Re-ID. Moreover, in the training process, we adopt the joint learning scheme to simultaneously train each branch by the independent loss function, which can enhance the generalization ability of each branch. Besides, a global loss function is utilized to further fine-tune the final concatenated feature. The extensive experiments on multiple benchmark datasets fully show that the proposedmethod can outperform the state-of-the-art person Re-ID methods. Furthermore, using greyscale images can indeed improve the person Re-ID performance in the proposed deep framework. [ABSTRACT FROM AUTHOR]