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7th International Conference on Advanced Algorithms and Control Engineering, ICAACE 2024

作者： Yan, Dapeng Bai, Chongzhi He, Lian Ding, Gangyi Key Laboratory Of Digital Performance And Simulation Technology Beijing Institute Of Technology Beijing China

ISBN: (纸本)9798350361445

Traditional social force models are capable of simulating various types of crowds, but due to their reliance on a single formula and simplified parameter control, they often lack realism when applied to crowd scenes. The integration of deep learning with social force models has already achieved success in pedestrian simulation tasks in specific scenarios, yet research in more generalized scenes is still lacking. Therefore, we have used deep learning in conjunction with social force models to construct crowd simulations in various scenarios. This model is based on the dynamics of crowd trajectories and refines key parameters of the social force model using deep learning methods. Experimental results prove that this model can effectively adapt to diverse environments different from the scenes on which the training data is based. © 2024 IEEE.

关键词： Deep learning

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Trajectory-BERT: Pre-training and fine-tuning bidirectional transformers for crowd trajectory enhancement

Trajectory-BERT: Pre-training and fine-tuning bidirectional ...

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作者： Li, Lingyu Huang, Tianyu Li, Yihao Li, Peng Key Laboratory of Digital Performance and Simulation Technology Beijing Institute of Technology Beijing China

To address the issue of trajectory fragments and ID switches caused by occlusion in dense crowds, we propose a space-time trajectory encoding method and a point-line-group division method to construct Trajectory-BERT in this paper. Leveraging the spatiotemporal context-dependent features of trajectories, we introduce pre-training and fine-tuning Trajectory-BERT tasks to repair occluded trajectories. Experimental results show that data augmented with Trajectory-BERT outperforms raw annotated data on the MOTA metric and reduces ID switches in raw labeled data, demonstrating the feasibility of our method. © 2023 John Wiley & Sons Ltd.

关键词： Trajectories

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Deep Learning Improved Social Force Model to Simulate Crowd in Varying Scenes

Deep Learning Improved Social Force Model to Simulate Crowd ...

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Advanced Algorithms and Control Engineering (ICAACE), International Conference on

作者： Dapeng Yan Chongzhi Bai Lian He Gangyi Ding Key Laboratory of Digital Performance and Simulation Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

关键词： Deep learning Training Adaptation models Pedestrians Force Training data Emergency services

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SES-SFM: A Heterogeneous Crowd simulation Model with Emotional Contagion

SES-SFM: A Heterogeneous Crowd Simulation Model with Emotion...

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Electronic technology and Information Science (ICETIS), International Conference on

作者： Chongzhi Bai Jiahui Lv Dapeng Yan Yunru Zhang Gangyi Ding Key Laboratory of Digital Performance and Simulation Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350388343

ISBN: (纸本)9798350388350

Addressing the issues of authenticity and complexity that are lacking in current homogenized crowd models, as well as the insufficiency in granularity of emotional heterogeneity methods leading to a lack of diversity, this paper proposes a heterogeneous crowd modeling approach. Referred to the Susceptible Infected Susceptible (SIS) model, a fine-grained emotional contagion model: Susceptible Emotional Susceptible (SES) is introduced. Combined the A * navigation algorithm, it is integrated with existing crowd modeling techniques to propose the Susceptible Emotional Susceptible-Social Force Model (SES-SFM). We have demonstrated the sensitivity relationship between the basic reproductive number $RO$ and the parameters within the SES model through our experiments. In comparative experiments, our model demonstrated superior collision resolution capabilities, operated more smoothly, and did not introduce excessive computational overhead when compared to traditional SFM. In simulation experiments, we have established the correlation between emotional factors and crowd behavior, thereby enriching the dynamics of crowd behavior. Consequently, we have made theoretical contributions to the field of crowd modeling techniques and the domain of emotional contagion.

关键词： Analytical models Information science Sensitivity Correlation Navigation Computational modeling Force

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A Method of Constructing Fine-Grained Pose Evaluation Model 1

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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Song, Zhen Zhang, Longfei Li, Haoyu Wu, Yufeng Ding, Gangyi Advanced Research Center for Digitalization of Traditional Drama of The Central Academy of Drama Beijing100710 China Key Laboratory of Digital Performance and Simulation Technology Beijing Institute of Technology Beijing100081 China

ISBN: (数字)9789819901050

ISBN: (纸本)9789819901043

In the field of computer vision, human pose estimation (HPE) is an important direction. However, in some fields that require professional actions or motions, researchers will deal with poses with high diversity, strongly instantaneous actions and complex behaviors, and with problems such as lack of enough dataset, algorithm iteration lag, and poor robustness, it is difficult for conventional CV methods to recognize many kinds of poses well. Aiming to the problems above, we propose a method of training fine-grained pose estimation model and building pose dataset based multi-views in this paper. And during the work, we use the operation of filtering as optimization approach to improve the accuracy of results. By filtering data, those wrongly recognized key points can be corrected to some extent and the missing key points or poses can be estimated based on the continuity of motion. The filter method can also improve the smoothness of curve, so the pose sequences can be more fluent and conform the kinetics after the operation. Meanwhile, there sometimes may be errors about the results of 3D pose reconstruction based on 2D pose because of occlusions, changes of environment. In this situation, choosing 2D pose results from appropriate views instead of all the views can also improve the accuracy of final results. Moreover, for the environment with several persons, we finish the work of matching human cross-different views by combining with the method of predicting depths of root key points. From the experiments, the method can increase the average precision (AP) up to 2.2%, which demonstrate the feasibility and reusability of our method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Reusability

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Generating Natural Pedestrian Crowds by Learning Real Crowd Trajectories Through a Transformer-Based Gan

SSRN

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SSRN 2023年

作者： Yan, Dapeng Ding, Gangyi Huang, Kexiang Huang, Tianyu Key Laboratory of Digital Performance and Simulation Technology Beijing Institute of Technology Beijing100081 China

Traditional methods for constructing crowd simulations often have shortcomings in terms of realism, and data-driven methods are an effective approach to enhancing the visual realism of crowd simulation. However, existing work mainly constructs crowd simulations through prediction-based approaches based on deep learning or by fitting the parameters of traditional methods, which limits the expressiveness of the model. In response to these limitations, this paper introduces a method capable of generating realistic pedestrian crowds. This approach uses a Generative Adversarial Network, complemented by a Transformer module, to learn behavioral patterns from actual crowd trajectories. We use a transformer module to extract trajectory features of the crowd, then convert the spatial relationships between individuals into sequences using a special data processing mechanism, and use the transformer module to extract social features of the crowd, while guiding the movement of each individual with their target direction. During training, we simultaneously learn from real crowd data and simulation data resolving collisions by traditional methods, to enhance the collision avoidance behavior of virtual crowds while maintaining the movement patterns of real crowds, resulting in more general collision avoidance behavior. The crowds generated by the model are not limited to specific scenarios and show generalization capabilities. Compared to other models, our method shows better performance on publicly available large-scale pedestrian datasets after training. © 2023, The Authors. All rights reserved.

关键词： Collision avoidance

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Introducing Anisotropic Fields for Enhanced Diversity in Crowd simulation

arXiv

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arXiv 2024年

作者： Li, Yihao Liu, Junyu Guan, Xiaoyu Hou, Hanming Huang, Tianyu Key Laboratory of Digital Performance and Simulation Technology Beijing Institute of Technology Zhong Guan Cun South Street Beijing100081 China

Large crowds exhibit intricate behaviors and significant emergent properties, yet existing crowd simulation systems often lack behavioral diversity, resulting in homogeneous simulation outcomes. To address this limitation, we propose incorporating anisotropic fields (AFs) as a fundamental structure for depicting the uncertainty in crowd movement. By leveraging AFs, our method can rapidly generate crowd simulations with intricate behavioral patterns that better reflect the inherent complexity of real crowds. The AFs are generated either through intuitive sketching or extracted from real crowd videos, enabling flexible and efficient crowd simulation systems. We demonstrate the effectiveness of our approach through several representative scenarios, showcasing a significant improvement in behavioral diversity compared to classical methods. Our findings indicate that by incorporating AFs, crowd simulation systems can achieve a much higher similarity to real-world crowd systems. Our code is publicly available at https://***/tomblack2014/AF Generation. Copyright © 2024, The Authors. All rights reserved.

关键词： Anisotropy

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Modeling of Drama performance Intelligent Evaluation Driven by Multimodal Data 15th

Modeling of Drama Performance Intelligent Evaluation Driven...

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15th International Conference on Genetic and Evolutionary Computing, ICGEC 2023

作者： Song, Zhen Wu, Yufeng Zhang, Longfei Tao, Wenting Li, Lijie Ding, Gangyi Advanced Research Center for Digitalization of Traditional Drama The Central Academy of Drama 39 Dongmian Hutong Dongcheng District Beijing China Key Laboratory of Digital Performance and Simulation Technology Beijing Institute of Technology 5 Zhongguancun South Street Haidian District Beijing China Military Dance Performance Teaching and Research Office School of Military Culture National Defense University of the People’s Liberation Army of China 18 Zhongguancun South Street Haidian District Beijing China

ISBN: (纸本)9789819700677

The purpose of this study is to explore a data-driven intelligent evaluation method for drama performances, and to improve the evaluation quality of drama performances. Our research work is mainly to establish the temporal relationship between motion and musical features in dramatic performances and to construct a multimodal evaluation dataset (PEMD, performance Evaluation Multimodal Dataset) for drama performances based on computer vision methods and deep learning technologies. Then the evaluation of drama performance is achieved by detecting and evaluating the match degree (DMD, Dramatic Match Degree) of the motion and musical features in the drama performance. The main works includes: (1) A data-driven intelligent evaluation framework for drama performance is proposed, which defines and describes the collection method, classification and feature extraction of drama performance evaluation data;(2) A sliding window computing unit based on Dramatic Stylization Annotation is proposed. As the core computing module of the drama performance evaluation architecture, it establishes the corresponding relationship between performance motion and music based on temporal features, and constructs Multimodal Evaluation Dataset (PEMD) for the drama performance;(3) Aiming at the temporal features of drama performances, a co-training method is proposed to establish the Theater performance Evaluation Model (TPEM) and realize intelligent computing methods for drama performance intelligent evaluation. The experimental results show that the average accuracy rate (MAP Mean Average Precision) of the drama performance evaluation model proposed in this paper reaches 62.41%, showing excellent evaluation ability. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Feature extraction

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A Review of Intelligent Vision Enhancement technology for Battlefield

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Wu, Yufeng He, Yanjun Li, Ning Wang, Guowei Zhang, Longfei Zhang, Fuquan Zhu, Yifan The 15th Research Institute China Electronics Technology Group Corporation Beijing100083 China Key Laboratory of Digital Performance and Simulation Technology Beijing Institute of Technology Beijing100081 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Department of Computer Science and Technology Tsinghua University Beijing100081 China

This paper reviews the application of intelligent vision enhancement technology in the battlefield environment and explores new research directions. This paper mainly introduces three parts. First, we introduce a solution for enhancing the battlefield situation and using head-mounted displays for soldier combat. Second, we summarize three core technologies supporting intelligent vision enhancement technology: 3D environment reconstruction, tracking and registration, and situational awareness. Third, we summarize three application directions of intelligent vision enhancement technology on the battlefield. Finally, the problems and challenges in the research work are proposed, including current issues such as accurate battlefield situational awareness by augmented reality technology, multiperson collaborative management and data flow, as well as challenges in future battlefield situation enhancement and perception. The future development trend of intelligent vision enhancement technology on the battlefield has been prospected. Two meanings of this paper are as follows: the first is to review the research status of intelligent vision enhancement technology from the technical level and identify the key technical points that may restrict development in the future;the second is to analyze the advantages and disadvantages of intelligent vision enhancement technology from the level of battlefield application and the roles of users. In addition, this paper proposes how to take the lead and take initiative in future wars. © 2023 Yufeng Wu et al.

关键词： Augmented reality

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performance Creativity Enhancement Method Based on Emotional Analysis and Concentration

Journal of Network Intelligence

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Journal of Network Intelligence 2023年第3期8卷 1019-1048页

作者： Liu, Chang Zhang, Longfei Huang, Tianyu Wu, Yufeng Zhang, Fuquan Lin, Qi Department of Computer Science and Technology Beijing Institute of Technology 100081 China Beijing Key Laboratory of Digital Performance and Simulation Technology 100081 China College of Computer and Control Engineering Minjiang University Fuzhou350108 China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu610021 China Informatics King’s College London LondonWC2R 2LS United Kingdom

Intelligent performance creativity is a new research direction of the intersection of technology and art. At present, the cutting-edge technologies such as computer simulation, emotional computing and machine learning have been applied to the evaluation and enhancement of intelligent performance creativity. The application of these technologies has greatly increased the diversity of performance forms and the complexity of performance content. For the traditional stage performance, due to the lack of timely feedback from the audience, it is difficult for the director to enhance the creativity effec-tively. In order to solve this problem, this paper proposes concentration level enhancement method (CLEM). The CLEM extracts and analyzes the physiological and emotional characteristics of the audience by collecting the multimodal physiological signal data of the audience. Through the analysis of the features, the concentration information of the audience is obtained and the concentration level (CL) is defined. Based on the physiological and emotional induction mechanism, a performance creativity enhancement strategy is proposed, which enhances the performance creativity in two aspects: " Visual-based" and " Content-based". Through the concentration level enhancement method, compare the audience’s performance attention changes before and after the performance creativity enhancement, select the effective strategies to enhance the performance creativity, and provide the director with quantifiable performance creativity enhancement feedback results. The experimental results show that the CLEM proposed in this paper can effectively induce significant changes in the audience’s emotion, and the selected creative enhancement strategy can significantly improve the audience’s concentration and achieve the purpose of enhancing the creativity of the performance. © 2023, Journal of Network Intelligence.

关键词： Physiology

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