Person re‐identification (Re‐ID) is a fundamental subject in the field of the computer vision technologies. The traditional methods of person Re‐ID have difficulty in solving the problems of person illumination, occlusion and attitude change under complex background. Meanwhile, the introduction of deep learning opens a new way of person Re‐ID research and becomes a hot spot in this field. This study reviews the traditional methods of person Re‐ID, then the authors focus on the related papers about different person Re‐ID frameworks on the basis of deep learning, and discusses their advantages and disadvantages. Finally, they propose the direction of further research, especially the prospect of person Re‐ID methods based on deep learning.

Gait is a unique perceptible biometric feature at larger distances, and the gait representation approach plays a key role in a video sensor-based gait recognition system. Class Energy Image is one of the most important gait representation methods based on appearance, which has received lots of attentions. In this paper, we reviewed the expressions and meanings of various Class Energy Image approaches, and analyzed the information in the Class Energy Images. Furthermore, the effectiveness and robustness of these approaches were compared on the benchmark gait databases. We outlined the research challenges and provided promising future directions for the field. To the best of our knowledge, this is the first review that focuses on Class Energy Image. It can provide a useful reference in the literature of video sensor-based gait representation approach.

Crowd counting has substantial practical applications in various consumer-oriented areas, particularly for safety assessments and marketing strategies. However, considering the complexities of the capturing conditions, the unavoidable background interference possesses the potential to disrupt the effectiveness of established counting methods, and it further poses degraded counting performance. To address this challenge, we propose a Region-Aware Quantum Network (RAQNet) by attentively learning from the crowd region. It consists of four key components, namely the feature extractor, the object region awareness module (ORA), the quantum-driven calibration (QDC) module, and the decoder module. The cascaded ORA modules are engineered for the extraction of local information, which addresses background interference. Additionally, two QDC modules are incorporated to capture global information, which utilizes quantum states to calibrate features. Extensive experimental results conducted on four crowd benchmark datasets and three cross-domain datasets prove that the RAQNet outperforms the state-of-the-art competitors, both subjectively and objectively.

Existing gait recognition methods are capable of extracting rich spatial gait information but often overlook fine-grained temporal features within local regions and temporal contextual information across different sub-regions. Considering gait recognition as a fine-grained recognition task and each individual exhibits uniqueness in their movements across different temporal sequences, we propose a local multi-scale and global contextual spatio-temporal (LMGCS) network for gait recognition. It divides the whole gait sequence into sub-sequences with multiple spatio resolutions and extracts multi-scale temporal features. We extract the temporal context information of different sub-sequences with the transformer, and all sub-sequences are fused to form global features. Furthermore, the loss function that combines the triplet loss function and cross-entropy loss function is utilized to prompt the proposed model to fulfill the gait recognition. The proposed method achieved state-of-the-art results on two popular public datasets. It achieved rank-1 accuracy of 98.0%, 95.4%, and 85.0% on the three walk states of the CASIA-B dataset and 90.9% on the OU-MVLP dataset.

We present a deformable generator model to disentangle the appearance and geometric information for both image and video data in a purely unsupervised manner. The appearance generator network models the information related to appearance, including color, illumination, identity or category, while the geometric generator performs geometric warping, such as rotation and stretching, through generating deformation field which is used to warp the generated appearance to obtain the final image or video sequences. Two generators take independent latent vectors as input to disentangle the appearance and geometric information from image or video sequences. For video data, a nonlinear transition model is introduced to both the appearance and geometric generators to capture the dynamics over time. The proposed scheme is general and can be easily integrated into different generative models. An extensive set of qualitative and quantitative experiments shows that the appearance and geometric information can be well disentangled, and the learned geometric generator can be conveniently transferred to other image datasets that share similar structure regularity to facilitate knowledge transfer tasks.