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Since gait can be utilized to identify individuals from a far distance without their interaction and coordination, recently many gait recognition methods have been proposed. However, due to a real-world scenario of clothing changes, a degradation occurs for most of these methods. Thus in this paper, a more efficient gait recognition method is proposed to address the problem of clothing variances. First, part-based gait features are formulated from two different perspectives, i.e., the separated body parts that are more robust to clothing changes and the estimated human skeleton key-point regions. It is reasonable to formulate such features for cloth-changing gait recognition, because these two perspectives are both less vulnerable to clothing changes. Given that each feature has its own advantages and disadvantages, a more efficient gait feature is generated in this paper by assembling these two features together. Moreover, since local features are more discriminative than global features, in this paper more attention is focused on the local short-range features. Also, unlike most methods, in our method we treat the estimated key-point features as a set of word embeddings, and a transformer encoder is specifically used to learn the dependence of each correlative key-points. The robustness and effectiveness of our proposed method are certified by experiments on CASIA Gait Dataset B, and it has achieved the state-of-the-art performance on this dataset. [ABSTRACT FROM AUTHOR]

Person re-identification aims to match person captured by multiple non-overlapping cameras that mainly mean standard RGB cameras. In contemporary surveillance, cameras of different modalities such as infrared cameras and depth cameras are introduced because of their unique advantages in poor illumination scenarios. However, re-identifying the persons across such cameras of different modalities is extremely difficult and, unfortunately, seldom discussed. It is mainly caused by extremely different appearances of the person shown under such different camera modalities. In this paper, we tackle this challenging cross-modality people re-identification through a top-push constrained modality-adaptive dictionary learning. The proposed model asymmetrically projects the heterogeneous features from dissimilar modalities onto a common space. In this way, the modality-specific bias is mitigated. Thus, the heterogeneous data can be simultaneously enforced by a shared dictionary in a canonical space. Moreover, a top-push ranking graph regularization is embedded in the proposed model to improve the discriminability, which efficiently further boosts the matching accuracy. In order to implement the proposed model, an iterative process is developed in this paper to optimize these two processes jointly. Extensive experiments on the benchmark SYSU-MM01 and BIWI RGBD-ID person re-identification datasets show promising results which outperform state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Current person re-identification (re-ID) works mainly focus on the short-term scenario where a person is less likely to change clothes. However, in the long-term re-ID scenario, a person has a great chance to change clothes. A sophisticated re-ID system should take such changes into account. To facilitate the study of long-term re-ID, this paper introduces a large-scale re-ID dataset called “Celeb-reID” to the community. Unlike previous datasets, the same person can change clothes in the proposed Celeb-reID dataset. Images of Celeb-reID are acquired from the Internet using street snap-shots of celebrities. There is a total of 1,052 IDs with 34,186 images making Celeb-reID being the largest long-term re-ID dataset so far. To tackle the challenge of cloth changes, we propose to use vector-neuron (VN) capsules instead of the traditional scalar neurons (SN) to design our network. Compared with SN, one extra-dimensional information in VN can perceive cloth changes of the same person. We introduce a well-designed ReIDCaps network and integrate capsules to deal with the person re-ID task. Soft Embedding Attention (SEA) and Feature Sparse Representation (FSR) mechanisms are adopted in our network for performance boosting. Experiments are conducted on the proposed long-term re-ID dataset and two common short-term re-ID datasets. Comprehensive analyses are given to demonstrate the challenge exposed in our datasets. Experimental results show that our ReIDCaps can outperform existing state-of-the-art methods by a large margin in the long-term scenario. The new dataset and code will be released to facilitate future researches. [ABSTRACT FROM AUTHOR]

Boosting algorithms, especially AdaBoost, have attracted great attention in computer vision. In the early version of boosting algorithms, the weak classifier selection and the strong classifier learning are linked together. It has been demonstrated that decoupling of these two processes can provide more flexibility for training a better classifier. In these studies, linear discriminant analysis (LDA) has been adopted to select weak classifiers independently based on class separability rather than a training error that occurs normally in AdaBoost. It is observed that LDA is successful only if a large number of labeled training samples is available. However, a large-scale labeled training set is not always available in many computer vision applications such as object classification. To tackle this problem, this paper proposes semisupervised subspace learning combined with a boosting framework for object classification, through which unlabeled data can participate in the boosting training to compensate for the lack of enough labeled data. With the proposed framework, this paper develops three various approaches that utilize unlabeled data in different ways. According to the experiments on several public image data sets, the proposed methods achieve superior performance over AdaBoost and existing semisupervised algorithms. [ABSTRACT FROM PUBLISHER]