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Rethinking Appearance-Based Deep Gait Recognition: Reviews, Analysis, and Insights From Gait Recognition Evolution

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2025年第6期36卷 9777-9797页

作者： Li, Jingqi Zhang, Yuzhen Zeng, Yi Ye, Changxin Xu, Wenzheng Ben, Xianye Wang, Fei-Yue Zhang, Junping Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Shandong Univ Shenzhen Res Inst Shenzhen 518063 Peoples R China Shandong Univ Sch Informat Sci & Engn Jinan 250100 Peoples R China Obuda Univ DeSci Ctr Parallel Intelligence H-1034 Budapest Hungary Macau Univ Sci & Technol Intelligent Syst Robot & Automat Lab Macau Peoples R China CASIA State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

Gait recognition is a prominent biometric recognition technique extensively employed in public security. Appearance-based and model-based gait recognition are two categories of methods commonly used. Specifically, appearance-based methods, which use silhouettes to represent body information, typically outperform model-based methods that rely on skeleton data, making them more popular. Recently, the shift from single-frame templates to multiframe silhouettes has advanced appearance-based gait recognition with better spatiotemporal representation. However, there is a notable lack of comprehensive studies that deepen the understanding of multiframe appearance-based gait recognition methods. This article reviews various methods to trace the evolution of gait recognition. Furthermore, we unify various performant models in one framework, study the overlooked effects on data arrangement, and explore the scaling ability of existing methods. Besides the advancement in gait recognition, we also summarize the current challenges and future prospects to foster future research.

关键词： Gait recognition Feature extraction Data models Reviews Training Skeleton Shape Analytical models Deep learning Clothing Appearance-based biometric deep learning gait recognition GaitBase

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Decentralized Autonomous Operations and Organizations in TransVerse: Federated Intelligence for Smart Mobility

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2023年第4期53卷 2062-2072页

作者： Zhao, Chen Dai, Xingyuan Lv, Yisheng Niu, Jinglong Lin, Yilun Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automation State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China North Automat Control Technol Inst Land Based Unmanned Syst Res & Dev Dept Taiyuan 030006 Peoples R China Shanghai AI Lab Urban Comp Res Ctr Shanghai 200232 Peoples R China

Human and social factors are essential to transportation systems, yet top-down management fails to consider them sufficiently. Consequently, management strategies are not tailored to human needs and are inadequate in providing transportation intelligence. This article investigates a management architecture based on decentralized/distributed autonomous operations/organizations (DAOs) that considers both the technical and societal aspects in our transportation metaverse, TransVerse. This design maps people's transportation needs in physical space to their digital counterparts in cyberspace, utilizing blockchain technology to guarantee the secure exchange of information and ultimately bring about the Internet of Minds (IoM). With the federated intelligence that emerged in IoM, we can devise reliable and prompt traffic decisions by incorporating consensus, community voting, and smart contracts into the organizational, coordination, and execution structure. Details on operational procedures and key technologies are also covered. To demonstrate the efficacy of DAOs-based management, a case study of world model-driven cooperative signal control is provided, indicating its promising application in future transportation management.

关键词： Transportation Behavioral sciences Organizations Cyberspace Social factors Roads Vehicle dynamics Artificial systems computational experiments parallel execution (ACP) blockchain cyber-physical-social systems (CPSS) decentralized distributed autonomous operations and organizations (DAOs) intelligent transportation systems (ITS)

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Pavement Cracks Coupled With Shadows:A New Shadow-Crack Dataset and A Shadow-Removal-Oriented Crack Detection Approach

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IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1593-1607页

作者： Lili Fan Shen Li Ying Li Bai Li Dongpu Cao Fei-Yue Wang School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Civil Engineering Tsinghua UniversityBeijing 100084China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body and the College of Mechanical and Vehicle Engineering Hunan UniversityChangsha 410082China School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving *** task is challenging because the shadows on the pavement may have similar intensity with the crack,which interfere with the crack detection *** to the present,there still lacks efficient algorithm models and training datasets to deal with the interference brought by the *** fill in the gap,we made several contributions as ***,we proposed a new pavement shadow and crack dataset,which contains a variety of shadow and pavement pixel size *** also covers all common cracks(linear cracks and network cracks),placing higher demands on crack detection ***,we designed a two-step shadow-removal-oriented crack detection approach:SROCD,which improves the performance of the algorithm by first removing the shadow and then detecting *** addition to shadows,the method can cope with other noise ***,we explored the mechanism of how shadows affect crack *** on this mechanism,we propose a data augmentation method based on the difference in brightness values,which can adapt to brightness changes caused by seasonal and weather ***,we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters,and the algorithm improves the performance of the model *** compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset,and the experimental results demonstrate the superiority of our method.

关键词： Automatic pavement crack detection data augmentation compensation deep learning residual feature augmentation shadow removal shadow-crack dataset

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Personalized gait trajectory generation based on anthropometric features using Random Forest

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Journal of Ambient Intelligence and Humanized Computing 2023年第12期14卷 15597-15608页

作者： Ren, Shixin Wang, Weiqun Hou, Zeng-Guang Chen, Badong Liang, Xu Wang, Jiaxing Peng, Liang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China

Using lower limb rehabilitation robots (LLRRs) to help stroke patients recover their walking ability is attracting more and more attention presently. Previous studies have shown that gait rehabilitation training with natural gait pattern can improve the therapeutic outputs. However, how to generate the personalized gait trajectory has not been well researched. In this paper, a personalized gait generation method based anthropometric features is proposed. Firstly, gait trajectories are fitted and simplified into Fourier coefficient vectors, which are used to represent gait trajectories. Secondly, fourteen body features are used to generate the personalized gait trajectories and the feature set is further optimized based on the minimal redundancy maximal relevance criterion for easy application on the LLRR. Then, the relationship between the optimized feature set and gait trajectories is modeled by using the RF algorithm. Finally, the performance of the proposed method is demonstrated by several comparison experiments. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Trajectories

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Fugl-Meyer hand motor imagination recognition for brain-computer interfaces using only fNIRS

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complex & INTELLIGENT systems 2022年第2期8卷 731-741页

作者： Li, Chenguang Yang, Hongjun Cheng, Long Chinese Acad Sci Inst Automat State Key Lab Control & Management Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

As a relatively new physiological signal of brain, functional near-infrared spectroscopy (fNIRS) is being used more and more in brain-computer interface field, especially in the task of motor imagery. However, the classification accuracy based on this signal is relatively low. To improve the accuracy of classification, this paper proposes a new experimental paradigm and only uses fNIRS signals to complete the classification task of six subjects. Notably, the experiment is carried out in a non-laboratory environment, and movements of motion imagination are properly designed. And when the subjects are imagining the motions, they are also subvocalizing the movements to prevent distraction. Therefore, according to the motor area theory of the cerebral cortex, the positions of the fNIRS probes have been slightly adjusted compared with other methods. Next, the signals are classified by nine classification methods, and the different features and classification methods are compared. The results show that under this new experimental paradigm, the classification accuracy of 89.12% and 88.47% can be achieved using the support vector machine method and the random forest method, respectively, which shows that the paradigm is effective. Finally, by selecting five channels with the largest variance after empirical mode decomposition of the original signal, similar classification results can be achieved.

关键词： Brain-computer interface Functional near-infrared spectroscopy (fNIRS) Motor imagery Classification Empirical mode decomposition

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Empirical Policy Optimization for n-Player Markov Games

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第10期53卷 6443-6455页

作者： Zhu, Yuanheng Li, Weifan Zhao, Mengchen Hao, Jianye Zhao, Dongbin Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Huawei Noahs Ark Lab Beijing 100085 Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin 300072 Peoples R China

In single-agent Markov decision processes, an agent can optimize its policy based on the interaction with the environment. In multiplayer Markov games (MGs), however, the interaction is nonstationary due to the behaviors of other players, so the agent has no fixed optimization objective. The challenge becomes finding equilibrium policies for all players. In this research, we treat the evolution of player policies as a dynamical process and propose a novel learning scheme for Nash equilibrium. The core is to evolve one's policy according to not just its current in-game performance, but an aggregation of its performance over history. We show that for a variety of MGs, players in our learning scheme will provably converge to a point that is an approximation to Nash equilibrium. Combined with neural networks, we develop an empirical policy optimization algorithm, which is implemented in a reinforcement-learning framework and runs in a distributed way, with each player optimizing its policy based on own observations. We use two numerical examples to validate the convergence property on small-scale MGs, and a pong example to show the potential on large games.

关键词： Continuous-time learning dynamics (CTLD) Markov game (MG) n-player Nash equilibrium policy optimization

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Learning Lightweight Dynamic Kernels With Attention Inside via Local-Global Context Fusion

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING systems 2024年第7期35卷 8984-8998页

作者： Tian, Yonglin Shen, Yu Wang, Xiao Wang, Jiangong Wang, Kunfeng Ding, Weiping Wang, Zilei Wang, Fei-Yue Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Nantong Univ Sch Informat Sci & Technol Nantong 226019 Peoples R China Univ Science & Technol China Natl Engn Lab Brain inspired Intelligence Technol Hefei 230027 Peoples R China

Traditional convolutional neural networks (CNNs) share their kernels among all positions of the input, which may constrain the representation ability in feature extraction. Dynamic convolution proposes to generate different kernels for different inputs to improve the model capacity. However, the total parameters of the dynamic network can be significantly huge. In this article, we propose a lightweight dynamic convolution method to strengthen traditional CNNs with an affordable increase of total parameters and multiply-adds. Instead of generating the whole kernels directly or combining several static kernels, we choose to "look inside ", learning the attention within convolutional kernels. An extra network is used to adjust the weights of kernels for every feature aggregation operation. By combining local and global contexts, the proposed approach can capture the variance among different samples, the variance in different positions of the feature maps, and the variance in different positions inside sliding windows. With a minor increase in the number of model parameters, remarkable improvements in image classification on CIFAR and ImageNet with multiple backbones have been obtained. Experiments on object detection also verify the effectiveness of the proposed method.

关键词： Attention inside kernels dynamic convolution global context local context

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From RAG/RAT to SAGE for Social Transportation: A CoT and New Perspective on Smart Logistics and Mobility

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第6期9卷 5005-5008页

作者： Fei-Yue Wang Wuhan Institute of Industrial Innovation and Development Hubei China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

Dear All

关键词： Intelligent vehicles Transportation Logistics

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Building Supporting SLAM Community for IEEE TIV: From DHWs to Smart Academic Organizations

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第7期9卷 5119-5123页

作者： Fei-Yue Wang Wuhan Institute of Industrial Innovation and Development Hubei China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

I would like to share with you the following information

关键词： Simultaneous localization and mapping Intelligent vehicles Buildings Organizations

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From RAG/RAT to SAGE: Parallel Driving for Smart Mobility

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第5期9卷 4821-4825页

作者： Fei-Yue Wang Wuhan Institute of Industrial Innovation and Development Wuhan China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

The current issue includes 2 perspectives, 2 letters, and 12 regular papers. These perspectives explore critical issues within the field of IVs and pontential research directions based on the evolution of foundation m... 详细信息

关键词： Intelligent vehicles

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