As an important part of intelligent surveillance systems, person re-identification (PReID) has drawn wide attention of the public in recent years. Many recent deep learning-based PReID methods have used attention or multi-scale feature learning modules to enhance the discrimination of the learned deep features. However, the attention mechanisms may lose some important feature information. Moreover, the multi-scale models usually embed the multi-scale feature learning module into the backbone network, which increases the complexity of testing network. To address the two issues, we propose a multi-scale deep supervision with attention feature learning deep model for PReID. Specifically, we introduce a reverse attention module to remedy the feature information losing issue caused by the attention module, and a multi-scale feature learning layer with deep supervision to train the network. The proposed modules are only used at the training phase and discarded during the test phase. Experiments on Market-1501, DukeMTMC-reID, CUHK03 and MSMT17 datasets. demonstrate that our model notably beats other competitive state-of-the-art models.

Person re-identification (PReID) has received increasing attention due to its significantly importance in intelligent video surveillance. However, most existing multi-scale feature learning methods embed the multi-scale feature extraction modules for PReID, which increases the complexity of inference network and reduces the timeliness. Moreover, jointly using the small-scale and large-scale features to learn feature representations may weaken the local detailed features extraction and spatial information learning. Besides, some attentive local features are often suppressed when introducing the attention mechanisms for deep PReID models. To address these issues, a deep model with Multi-scale Feature Representation Learning (MFRL) and Random Batch Feature Mask (RBFM) is proposed for PReID in this study. To ensure the feature representations discriminability and spatial information learning, two identity losses are adopted to supervise the small-scale and large-scale features learning in MFRL module, respectively. To alleviate the situation of local attentive features being suppressed by using attention mechanisms, RBFM branch with random feature block dropping strategy which can learn the attentive local feature representations. The proposed methods are only performed in the training phase and discarded in the testing phase, thus, enhancing the effectiveness of the model. Our model achieves the state-of-the-art on the popular benchmark datasets including Market-1501, DukeMTMC-reID and CUHK03. Besides, we conduct a set of ablation experiments to verify the effectiveness of the proposed methods.

Vehicle tracking detection, recognition and counting is an important part of vehicle analysis. Designing such a model with excellent performance is difficult. The traditional target detection algorithm based on artificial features has poor generalization ability and robustness. In order to take use the deep learning method for vehicle tracking detection, recognition and counting, this paper proposes a vehicle detection method based on yolov5. This method uses the deep learning technology, takes the running vehicles video as the research object, analysis the target detection algorithm, proposes a vehicle detection framework and platform. The relevant detection algorithm of the platform designed in this paper has great adaptability, when displayed under various conditions, such as heavy traffic, night environment, multiple vehicles overlap with each other, partial loss of vehicles, etc. it has good performance. The experimental results show that the algorithm can accurately segment and identify vehicles according to the edge contour of vehicles. It can take use the materials includes pictures, videos, and real-time monitoring, and has a high recognition rate in real-time performance.

The power of online social networks to propagate information within communities and from one community to the next is undeniable. Both network structure and information propagation affect each other; they restrict and cooperate with each other. However, they can also dynamically reshape the network topology of user’s social relationship in this process. The above process ultimately forms a feedback loop: the network structure affects how information spreads, while information propagation reshapes network topologies, so both evolve in concert over time. Using information propagation trees (IPT) of posts from the Sina Weibo microblogging site, we conducted a null model-based analysis to determine the influence of community structures on information propagation. We first generated randomized copies of the IPTs and then mined community structures from the originals and copies for comparison. An in-depth examination of the results in terms of improved significant profile, the length of information propagation path, and the relevance of the nodes in the propagation path indirectly reveals the inhibitory effect of community structures on information propagation.

Vehicle re-identification (Re-ID) has emerged as a critical challenge in the fields of computer vision and intelligent transportation systems, driven by the need to accurately identify vehicles across multiple camera views in diverse urban environments. Traditional methods relying on appearance-based features such as color and resemblance often fall short due to the variability in vehicle appearances caused by factors like illumination changes, camera angles, and occlusions. To address these limitations, this paper proposes a novel approach that leverages unsupervised learning, integrating agglomerative clustering techniques and style transfer via the StarGAN2 model. By treating each camera as a unique style domain, the proposed method generates new training pictures with diverse styles, reducing camera-induced variability and enhancing cross-camera vehicle identification. The repeated learning framework further refines vehicle identities by clustering similar pictures, thereby improving the system's ability to distinguish between vehicles in real-world scenarios. This approach not only minimizes the need for manual annotation but also enhances the robustness and adaptability of vehicle Re-ID systems, making it a valuable contribution to urban surveillance and intelligent transportation systems.

'Retraction Note to: Person Re-identification Based on Transform Algorithm' published in 'Intelligent Computing Theories and Application'