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15th Asian Conference on Computer Vision, ACCV 2020

作者： Wang, Li Fan, Baoyu Guo, Zhenhua Zhao, Yaqian Zhang, Runze Li, Rengang Gong, Weifeng Inspur Electronic Information Industry Co. Ltd. Jinan China State Key Laboratory of High-end Server Storage Technology Jinan China Qingdao China

ISBN: (纸本)9783030695439

For mass pedestrians re-identification (Re-ID), models must be capable of representing extremely complex and diverse multi-scale features. However, existing models only learn limited multi-scale features in a multi-branches manner, and directly expanding the number of scale branches for more scales will confuse the discrimination and affect performance. Because for a specific input image, there are a few scale features that are critical. In order to fulfill vast scale representation for person Re-ID and solve the contradiction of excessive scale declining performance, we proposed a novel Dense-Scale Feature Learning Network (DSLNet) which consist of two core components: Dense Connection Group (DCG) for providing abundant scale features, and Channel-Wise Scale Selection (CSS) module for dynamic select the most discriminative scale features to each input image. DCG is composed of a densely connected convolutional stream. The receptive field gradually increases as the feature flows along the convolution stream. Dense shortcut connections provide much more fused multi-scale features than existing methods. CSS is a novel attention module different from any existing model which calculates attention along the branch direction. By enhancing or suppressing specific scale branches, truly channel-wised multi-scale selection is realized. To the best of our knowledge, DSLNet is most lightweight and achieves state-of-the-art performance among lightweight models on four commonly used Re-ID datasets, surpassing most large-scale models. © 2021, Springer Nature Switzerland AG.

关键词： Convolution

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Risk Region-based Prediction Model for the Epidemic Spreading

Risk Region-based Prediction Model for the Epidemic Spreadin...

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International Conference on Computer Supported Cooperative Work in Design

作者： Fuwei Li Dianjie Lu Baoxu An Guijuan Zhang School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

Studying the spread process of epidemics in the crowd is a significant topic, which can help to take measures in advance to avoid epidemic spread and reduce the losses of life and property of the general public. However, this is a challenging problem that how to capture the spatio-temporal characteristics of the epidemic spreading. In this paper, we propose a risk region-based prediction model to characterize the spread feature. First, we propose a risk region-based spread model to describe the spatial relationship between individuals by dividing risk regions and assigning different spread characteristics to different risk regions. Second, we develop a spatio-temporal point process-based infection intensity quantification method to describe the variation of epidemic spread characteristics over time in different risk regions in the form of intensity. Then, we propose a long short-term memory (LSTM) based intensity function prediction method to solve the corresponding intensity and predict the spread process by the spatio-temporal point process (STPP). Finally, we implement an epidemic spreading simulation platform to verify our method and visualize the experimental results. The proposed model is expected to provide guidance for predicting the spread of epidemics.

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Deep reinforcement learning-based panic crowd evacuation simulation

Deep reinforcement learning-based panic crowd evacuation sim...

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International Conference on Computer Supported Cooperative Work in Design

作者： Baoxu An Guijuan Zhang Fuwei Li Nianyun Song Hong Liu Dianjie Lu School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

Crowd evacuation simulation can provide guidance for public emergencies or mass casualties which is easy to cause crowding and stampede. Deep reinforcement learning is an effective method for the path planning of crowd evacuation simulation which can reduce the dependence on data and has strong generalization. However, current methods of deep reinforcement learning ignore the consideration of panic emotion which could result in weak authenticity since the panic emotion has a significant impact on crowd evacuation. To address this problem, we propose a novel deep reinforcement learning for the panic crowd evacuation. First, a quadrant based crowd network is constructed to guide the movement of agents according to the change of panic degree in different quadrants. Second, a method of quantifying panic degree is proposed. Through the mean field equation, the dynamic evolution of agent states is realized, and the panic degree in different regions is quantified. Finally, a panic crowd evacuation model based on multi-agent deep deterministic policy gradient (MADDPG) was established. The computated panic degree is introduced into the reward function to recommend a path for multiagent to avoid panic. Experimental results show the effectiveness of our method.

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Spatial-temporal Consistency based Crowd Emotion Recognition

Spatial-temporal Consistency based Crowd Emotion Recognition

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International Conference on Computer Supported Cooperative Work in Design

作者： Chuanmiao Zhao Guijuan Zhang Lei Lv Hong Liu Dianjie Lu School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

In crowd evacuation scenarios, it is an effective way to ensure evacuation safety by recognizing real crowd emotions and then taking measures such as emotional infection to reduce crowd panic. However, factors such as distance, exposure, angle, and occlusion can lead to incomplete face expression information collection, thus failing to accurately identify crowd emotions. Therefore, it is still a very challenging problem to recognize individual emotions in the case of incomplete face expression information collection, and thus accurately identify crowd emotions during evacuation. To solve this problem, we propose a spatial-temporal consistency-based crowd emotion recognition method to accurately identify the real emotions of the crowd. First, for video frames that can capture the complete facial expression information, we use the residual network to accurately identify the individual emotion values in each frame. For video frames that cannot capture the complete facial expression information, we propose an individual emotion calculation model based on spatial-temporal consistency to calculate the individual emotion values in each frame. Second, we define the crowd panic level and obtain the real crowd emotion by calculation. Finally, we implement an end-to-end crowd panic emotion recognition system to verify our method. The experimental results show that the method can accurately calculate the crowd panic level, which is important for guiding crowd evacuation.

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An Epidemic Model Based on Intra- and Inter-group Interactions

An Epidemic Model Based on Intra- and Inter-group Interactio...

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International Conference on Computer Supported Cooperative Work in Design

作者： Wencong Geng Guijuan Zhang Dianjie Lu School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

The global spread of COVID-19 causes great losses to human society. Accurate calculation of the scale of epidemic spread is of great significance for the implementation of corresponding epidemic prevention measures. However, the existing method ignores the group formed by social relations of the population, which reduces the accuracy of the epidemic spread number calculation. In this paper, we propose an epidemic model based on intra- and inter-group interactions. Firstly, we construct a dual network model of epidemic spread based on intra- and inter-group interactions. The network describes how epidemics spread intra- and inter-group. To capture the intergroup influences, we construct a model for social mobility to calculate the inter-group spread rate. Secondly, we propose a computational model for the epidemic spread. We calculate the infection probability of groups in the upper layer network by using a continuous-time Markov chain (CTMC). We describe a dynamic evolution of the intra-group infection in the underlying network based on the mean field equation. And the number of infections in the population is calculated by integrating intra- and inter-group effects. Finally, we implement an epidemic spread simulation system to visualize the spread process. The experimental results show that the model can analyze the epidemic spread process more accurately.

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Abnormal Crowd Behavior Detection Based on the Fusion of Macro and Micro Features

Abnormal Crowd Behavior Detection Based on the Fusion of Mac...

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International Conference on Computer Supported Cooperative Work in Design

作者： Shourong Jiao Dianjie Lu Chuanhua Jia Hong Liu Guijuan Zhang School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

There are two types of research on abnormal crowd behavior detection: micro modeling methods (e.g., crowd pose) and macro modeling methods (e.g., optical flow methods). In crowd evacuation, however, micro methods fail to solve the occlusion problem, while macro methods fail to address issues such as poor real-time performance and non-adaptive motion target detection. To address these issues, we propose a method that combines macro and micro methods to detect abnormal crowd behavior. Firstly, we extract the two-dimensional poses of moving people using the human pose estimation algorithm (OpenPose) and get the corresponding micro motion features. Secondly, we create an optical flow map of the video using the dense optical flow algorithm (Farneback) and a scene activity map by comparing the optical flow sizes between two consecutive frames. Then we derive the entropy change curve, which represents the macro motion features. Finally, we fuse micro and macro motion features and put the fused hybrid feature vectors into our classification network to train our model, and then find the transition time from micro to macro abnormal states in crowd evacuation videos. The experimental findings demonstrate that our proposed method can more accurately and quickly identify the abnormal behaviors that appear in evacuation scenes.

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Multimodal Emotion Recognition Using CNN-SVM with Data Augmentation

Multimodal Emotion Recognition Using CNN-SVM with Data Augme...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Gengyuan Guo Pengzhi Gao Xiangwei Zheng Cun Ji School of Information Science and Engineering Shandong Normal University State Key Laboratory of High-end Server & Storage Technology Jinan China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

With the development of human-computer interaction and mobile sensors, emotion recognition based on physiological signals has aroused a lively discussion among scholars. The main difficulty faced is the small amount of data which leads to poor training results. In this paper, we proposed a multimodal emotion recognition using CNN-SVM and data augmentation (CSDAMER). Electrocardiography (ECG), galvanic skin response (GSR) and respiration (RSP) are utilized as input data, which are less requiring on the collection environment and can be collected by mobile sensors. To improve the training effect of model, data augmentation is performed by transformations, such as inversion, recombination and noise injection. Moreover, the convolutional layer of the convolutional neural network (CNN) is leveraged to extract the high-level features of the physiological signals, and then the features are input into the support vector machine (SVM) classifier to obtain the recognition results. The experimental results show that CSDAMER achieves 80.7% and 79.92% accuracy in arousal and valance, respectively. Compared with CNN alone, the accuracy of arousal and valance is increased by 12.87% and 9.95%. Meanwhile, the addition of the data augmentation improves the accuracy in arousal and valance by 21.94% and 25.73%.

关键词： Training Support vector machines Emotion recognition Electrocardiography Feature extraction Physiology Data models

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Singular Value Decomposition Based Pedestrian Trajectory Prediction

Singular Value Decomposition Based Pedestrian Trajectory Pre...

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International Conference on Computer Supported Cooperative Work in Design

作者： Heng Wang Guijuan Zhang Hong Liu Dianjie Lu School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

Numerous cameras deployed in venues can collect video data, which can be analyzed to help evacuate people in emergency situations. Most surveillance videos can provide data support for pedestrian trajectory prediction. However, data-driven prediction method do not consider the impact of personalization on pedestrian trajectories, which results in ignoring individual personalization. How to accurately analyze the differences among pedestrians and yet accurately predict pedestrian trajectories is a challenging problem. To solve the above problems, we propose an singular value decomposition (SVD) based pedestrian trajectory prediction method, which introduces matrix decomposition to pedestrian trajectory prediction for the first time. Thus, This method analyzes the impact of pedestrian personalization on motion trajectories by mining the interaction patterns between pedestrians and environment. Firstly, we propose an information collection method based on environmental semantics, which can extract scene information from historical video data to construct an environmental information matrix. Secondly, we propose an SVD-based individual environmental feature preference method, which uses singular value decomposition methods to mine the data and analyze personalized pedestrian motion patterns to construct a personalized individual preference matrix. Finally, we built an personalized trajectory prediction method to predict pedestrian movement trajectories. The experimental results show that the method can not only analyze the effect of personalization on pedestrian movement but also accurately predict the pedestrian movement trajectory.

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Optical trapping using copper nanotweezers

Optical trapping using copper nanotweezers

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Optical Sensors, Sensors 2021 - Part of OSA Optical Sensors and Sensing Congress 2021

作者： Xu, Zhe Li, Chen Li, Ruyang Zhao, Yaqian Li, Rengang Electronic Information Industry Co. Ltd. Beijing 100085 China 2State Key Laboratory of High-end Server & Storage Technology Jinan 250101 China

We demonstrate the optical trapping of single dielectric nanoparticles in a microfluidic chamber using a coupled T-shaped copper plasmonic nanoantenna for studying light–matter interactions. We present numerical simu... 详细信息

ISBN: (纸本)9781557528209

关键词： Copper

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Toward Big-Data Sharing: A Unified Trusted Remote Attestation Scheme based on Blockchain

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IEEE Internet of Things Journal 2025年

作者： Wang, Ran Ma, Fuqiang Duan, Shihong Su, Zhiyuan Zhang, Xiaotong Xu, Cheng University of Science and Technology Beijing School of Computer and Communication Engineering Shunde Innovation School China Inspur Data Technology Co. Ltd. Shandong Jinan China State Key Laboratory of High-end Server & Storage Technology Beijing China Inspur Electronic Information Industry Co. Ltd Shandong Jinan China

The rapid expansion of the Internet of Things (IoT) has brought forth new challenges and opportunities in securely managing and sharing vast amounts of data generated by connected devices. Blockchain technology, with its decentralization, tamper-resistance, and traceability, offers a promising framework for IoT data sharing but struggles to safeguard smart contracts and sensitive data. Integrating Trusted Execution Environments (TEEs) with blockchain addresses these concerns, enabling secure execution and communication via remote attestation. However, existing remote attestation methods face challenges, including incompatibility across heterogeneous TEEs, inefficiency under frequent authentication, and vulnerability to DoS attacks. To tackle these, we propose a blockchain-based unified remote attestation scheme for IoT. Our three-tier blockchain architecture–comprising a Certificate Authority (CA) channel, an authoritative channel, and a business channel–separates authentication, attestation, and operations while ensuring auditability. An abstraction layer supports heterogeneous TEEs, and an authoritative blockchain stores authentication reports, enabling secure, frequent attestations. Additionally, a distributed CA system enhances resilience to DoS attacks. Experimental results validate our scheme’s efficiency and security, offering a robust solution for IoT data sharing. © 2014 IEEE.

关键词： blockchain confidential computing data security data sharing trusted execution environment

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