Search

The large pose variations and misalignment errors exhibited by person images significantly increase the difficulty of person Re-Identification (ReID). Existing works commonly apply extra operations like pose estimation, part segmentation, etc., to alleviate those issues and improve the robustness of pedestrian representations. While boosting the ReID accuracy, those operations introduce considerable computational overheads and make the deep models complex and hard to tune. To chase a more efficient solution, we propose a Part-Guided Representation (PGR) composed of Pose Invariant Feature (PIF) and Local Descriptive Feature (LDF), respectively. We call PGR “Part-Guided” because it is trained and supervised by local part cues. Specifically, PIF approximates a pose invariant representation inferred by pose estimation and pose normalization. LDF focuses on discriminative body parts by approximating a representation learned with body region segmentation. In this way, extra pose extraction is only introduced during the training stage to supervise the learning of PGR, but is not required during the testing stage for feature extraction. Extensive comparisons with recent works on five widely used datasets demonstrate the competitive accuracy and efficiency of PGR. [ABSTRACT FROM AUTHOR]

Person re-identification is a crucial task of identifying pedestrians of interest across multiple surveillance camera views. For person re-identification, a pedestrian is usually represented with features extracted from a rectangular image region that inevitably contains the scene background, which incurs ambiguity to distinguish different pedestrians and degrades the accuracy. Thus, we propose an end-to-end foreground-aware network to discriminate against the foreground from the background by learning a soft mask for person re-identification. In our method, in addition to the pedestrian ID as supervision for the foreground, we introduce the camera ID of each pedestrian image for background modeling. The foreground branch and the background branch are optimized collaboratively. By presenting a target attention loss, the pedestrian features extracted from the foreground branch become more insensitive to backgrounds, which greatly reduces the negative impact of changing backgrounds on pedestrian matching across different camera views. Notably, in contrast to existing methods, our approach does not require an additional dataset to train a human landmark detector or a segmentation model for locating the background regions. The experimental results conducted on three challenging datasets, i.e., Market-1501, DukeMTMC-reID, and MSMT17, demonstrate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Background interference, which arises from complex environment, is a critical problem for a robust person re-identification (re-ID) system. The background noise may significantly compromise the feature learning and matching process. To reduce the background interference, this paper proposes a saliency image embedding as a pedestrian descriptor. First, to eliminate the background for each pedestrian image, the saliency image is constructed, which is implemented through an unsupervised manifold ranking-based saliency detection algorithm. Second, to reduce some errors and details missing of pedestrian during the saliency image construction process, a saliency image fusion (SIF) convolutional neural network (CNN) architecture is well designed, in which the original pedestrian image and saliency image are both employed as input. We implement our idea in the identification models based on some state-of-the-art backbone CNN models (i.e., CaffeNet, VGGNet-16, GoogLeNet and ResNet-50). We show that the learned pedestrian descriptor by the proposed SIF CNN architecture provides a significant improvement over the baselines and produces a competitive performance compared with the state-of-the-art person re-ID methods on three large-scale person re-ID benchmarks (i.e., Market-1501, DukeMTMC-reID and MARS). [ABSTRACT FROM AUTHOR]

• We decouple the difficulties affecting the person re-identification task into the Camera-Camera (CC) problem and the Camera-Person (CP) problem. • We propose a bi-stream generative model for solving the CC and CP problems separately, with promising results. • We design a part-weighted loss based on the unbalanced number of human body parts in the dataset to guide the model to focus on the more important parts. Generalizable person re-identification (re-ID) has attracted growing attention due to its powerful adaptation capability in the unseen data domain. However, existing solutions often neglect either crossing cameras (e.g., illumination and resolution differences) or pedestrian misalignments (e.g., viewpoint and pose discrepancies), which easily leads to poor generalization capability when adapted to the new domain. In this paper, we formulate these difficulties as: 1) Camera-Camera (CC) problem, which denotes the various human appearance changes caused by different cameras; 2) Camera-Person (CP) problem, which indicates the pedestrian misalignments caused by the same identity person under different camera viewpoints or changing pose. To solve the above issues, we propose a Bi-stream Generative Model (BGM) to learn the fine-grained representations fused with camera-invariant global feature and pedestrian-aligned local feature, which contains an encoding network and two stream decoding sub-network. Guided by original pedestrian images, one stream is employed to learn a camera-invariant global feature for the CC problem via filtering cross-camera interference factors. For the CP problem, another stream learns a pedestrian-aligned local feature for pedestrian alignment using information-complete densely semantically aligned part maps. Moreover, a part-weighted loss function is presented to reduce the influence of missing parts on pedestrian alignment. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods on the large-scale generalizable re-ID benchmarks, involving domain generalization setting and cross-domain setting. [ABSTRACT FROM AUTHOR]