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Synchrony Vision: Capturing Body Motion Synchrony Through Phase Difference Using the Kinect

IEEE ACCESS 2025年 13卷 41658-41669页

作者： Kwon, Jinhwan Kyoto Univ Educ Dept Educ Kyoto 6128522 Japan

This study introduces a Kinect-based system for real-time detection of body motion synchrony, addressing limitations of traditional methods such as high costs, intrusiveness, subjectivity, and lack of real-time analysis. Using a phase difference detection algorithm, the system analyzes synchrony offering a comprehensive framework for quantifying interpersonal coordination. The system captures acceleration data from up to six individuals, calculates phase differences, and provides immediate feedback. The original algorithm was modified to address challenges such as gravitational acceleration, inverted axes, and peak detection thresholds, improving the accuracy of Kinect data analysis. Experimental results demonstrate the system's overall detection accuracy of 89.2% under controlled conditions. Additionally, a comparison with an accelerometer-based method revealed a strong correlation (r = 0.73, p = 0.002), indicating alignment in detecting synchrony. While the Kinect-based system offers advantages in scalability and usability, it exhibits limitations in detecting high-speed movements. To enhance its accuracy, potential improvements include algorithmic refinements, hardware upgrades, and AI-driven models to adaptively refine motion detection.

关键词： Sensors Real-time systems Synchronization detection algorithms Tracking Costs Accuracy Sensor systems Optical sensors Data visualization Body motion synchrony Kinect-based system phase difference detection real-time analysis non-invasive systems

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Real-Time Monitoring of Personal Protective Equipment Compliance in Surveillance Cameras

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IEEE ACCESS 2024年 12卷 121882-121895页

作者： Al-Azani, Sadam Luqman, Hamzah Alfarraj, Motaz Sidig, Ala Addin I. Khan, Ayaz H. Al-Hamed, Dina King Fahd Univ Petr & Minerals SDAIA KFUPM Joint Res Ctr Artificial Intelligence Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Informat & Comp Sci Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Elect Engn Dept Dhahran 31261 Saudi Arabia King Fahd Univ Petr & Minerals Comp Engn Dept Dhahran 31261 Saudi Arabia

Worker safety in the industrial, construction, and energy sectors is the primary concern of governments, organizations, and project managers. Traditional monitoring systems are tedious, laborious, subjective, expensive, and inaccurate. Therefore, the development of automated and accurate systems for real-time monitoring and detecting personal protective equipment (PPE) compliance is urgently needed. This study provides valuable insights and contributions to the field of workers' safety in the industrial, construction, and energy sectors. It aims to evaluate the potential contribution of providing a robust and accurate real-time PPE compliance monitoring system using state-of-the-art deep learning techniques with real-world videos collected from surveillance cameras. Furthermore, it presents a systematic framework for developing, evaluating, and selecting the most suitable model for real-world applications, starting from data collection and ending with system deployment and maintenance. To the best of our knowledge, the collected dataset in this work is the largest PPE's dataset, with approximately 7.7 million labels representing different PPEs in 386,011 video frames collected from several surveillance cameras. The techniques adopted to generate different models and evaluated for PPE are CenterNet, Vision Transformer, and YOLOv7. Moreover, an optimized real-time inference engine is developed leveraging the high-computing power of GPUs in the system. The experimental results demonstrate that the models developed using YOLO are the most accurate and can be better generalized to detect PPE compliance in new working environments, achieving mean average precisions of 92.36% and 66.70% on Test Set I and Test Set II, respectively. In addition, they achieve real-time detection of PPE compliance.

关键词： Personal protective equipment Safety Head Cameras Real-time systems YOLO Videos detection algorithms Program processors Multithreading PPE monitoring PPE detection multithreading vision transformers objects as points

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Detecting and Analyzing Motifs in Large-Scale Online Transaction Networks

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2025年第2期37卷 584-596页

作者： Jiang, Jiawei Huang, Hao Zheng, Zhigao Wei, Yi Fu, Fangcheng Li, Xiaosen Cui, Bin Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Peking Univ Sch Comp Sci Beijing 100871 Peoples R China Tencen Inc Shenzhen 518054 Peoples R China

Motif detection is a graph algorithm that detects certain local structures in a graph. Although network motif has been studied in graph analytics, e.g., social network and biological network, it is yet unclear whether network motif is useful for analyzing online transaction network that is generated in applications such as instant messaging and e-commerce. In an online transaction network, each vertex represents a user's account and each edge represents a money transaction between two users. In this work, we try to analyze online transaction networks with network motifs. We design motif-based vertex embedding that integrates motif counts and centrality measurements. Furthermore, we design a distributed framework to detect motifs in large-scale online transaction networks. Our framework obtains the edge directions using a bi-directional tagging method and avoids redundant detection with a reduced view of neighboring vertices. We implement the proposed framework under the parameter server architecture. In the evaluation, we analyze different kinds of online transaction networks w.r.t the distribution of motifs and evaluate the effectiveness of motif-based embedding in downstream graph analytical tasks. The experimental results also show that our proposed motif detection framework can efficiently handle large-scale graphs.

关键词： Image edge detection Network motifs Social networking (online) Biology Servers Tagging Proteins Indexes Feature extraction detection algorithms Online transaction network network motif parameter server

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On the Road to Proactive Vulnerability Analysis and Mitigation Leveraged by Software Defined Networks: A Systematic Review

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IEEE ACCESS 2024年 12卷 98546-98566页

作者： Polonio, Joao Moura, Jose Neto Marinheiro, Rui Inst Univ Lisboa ISCTE IUL P-1649026 Lisbon Portugal Inst Telecomun IT P-1049001 Lisbon Portugal

The discovery of security vulnerabilities and their mitigation in networked systems managed by Software-Defined Networking (SDN) are fundamental for ensuring their normal operation. The main goal of this survey was to investigate the literature on preventing system security vulnerabilities instead of detecting ongoing cyber-attacks as quickly as possible. Thus, in our opinion, organizations should fortify their systems' security by identifying and eliminating any new security vulnerabilities before they can be successfully exploited. We comprehensively discuss different vulnerability detection approaches based on important comparison parameters such as vulnerability assessment, the SDN controller used, automation capability, system risk indicators, passive scanning and active probing of system vulnerabilities. The paper also analyzes relevant literature considering the mitigation mechanisms for discovered vulnerabilities such as the proposed SDN controller, automation capability, solution adaptation to system operational changes, risk indicators, and the solution's impact on network quality metrics like latency and throughput. Despite the strengths of the surveyed work, we have also identified promising open issues that need further consideration by scholars, industry participants, and policymakers. We concluded that the majority of analyzed literature contributions are largely reactive in their implementation against running network threats. This suggests a new research domain for applying SDN in the automatic detection of security vulnerabilities and their proactive mitigation before external cyber-attackers can exploit them.

关键词： Prevention and mitigation Software defined networking Measurement Surveys Reviews Road traffic detection algorithms Risk management Network security Network systems System vulnerability assessment detection mitigation software defined networks risk evaluation automatic operation network security

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Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting

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IEEE ACCESS 2024年 12卷 70676-70689页

作者： Li, Hao Rajbahadur, Gopi Krishnan Lin, Dayi Bezemer, Cor-Paul Jiang, Zhen Ming Univ Alberta Dept Elect & Comp Engn Edmonton AB T6G 2R3 Canada Huawei Technol Ctr Software Excellence Kingston ON K7L 1H3 Canada York Univ Dept Elect Engn & Comp Sci Toronto ON M3J 1P3 Canada

In software engineering, deep learning models are increasingly deployed for critical tasks such as bug detection and code review. However, overfitting remains a challenge that affects the quality, reliability, and trustworthiness of software systems that utilize deep learning models. Overfitting can be (1) prevented (e.g., using dropout or early stopping) or (2) detected in a trained model (e.g., using correlation-based approaches). Both overfitting detection and prevention approaches that are currently used have constraints (e.g., requiring modification of the model structure, and high computing resources). In this paper, we propose a simple, yet powerful approach that can both detect and prevent overfitting based on the training history (i.e., validation losses). Our approach first trains a time series classifier on training histories of overfit models. This classifier is then used to detect if a trained model is overfit. In addition, our trained classifier can be used to prevent overfitting by identifying the optimal point to stop a model's training. We evaluate our approach on its ability to identify and prevent overfitting in real-world samples. We compare our approach against correlation-based detection approaches and the most commonly used prevention approach (i.e., early stopping). Our approach achieves an F1 score of 0.91 which is at least 5% higher than the current best-performing non-intrusive overfitting detection approach. Furthermore, our approach can stop training to avoid overfitting at least 32% of the times earlier than early stopping and has the same or a better rate of returning the best model.

关键词： Training History Time series analysis Data models Hidden Markov models Measurement Training data Software engineering Deep learning Neural networks Computer bugs detection algorithms Trusted computing deep learning machine learning overfitting training history early stopping neural networks

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Enhancing Battery Exterior Defect Inspection Accuracy Through Defect-Background Separated GAN Development

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IEEE ACCESS 2024年 12卷 44286-44305页

作者： Ku, Donghun Pahk, Heui Jae Seoul Natl Univ Sch Mech & Aerosp Engn Seoul 08826 South Korea

This paper aims to develop a defect-background separated generative adversarial network (GAN) using deep learning and GAN to enhance the accuracy of battery exterior defect inspection. In actual battery production lines, the occurrence rates of defects vary by defect type, making it challenging to create a large, uniform defect dataset due to the time required for defect acquisition. This leads to a reduction in the accuracy of battery exterior defect inspection. To construct a large, uniform defect dataset, this paper proposes a defect-background separated GAN based on the principles of GANs. The defect-background separated GAN performs effective defect and background separation learning by referring to the segmentation labeling of defects. Through dataset augmentation using the defect-background separated GAN, the performance quality of newly generated synthetic defect images has improved, and accuracy in battery exterior defect inspection can be enhanced through extensive dataset training. Experimental results show the lowest Frechet inception distance score among various other methods for the battery exterior defect dataset, and it generates clear synthetic defects perceptible to the human eye. By training defect segmentation on this large, uniform defect dataset, an accuracy of 96.1% and an intersection over union value of 0.71 were achieved. Ultimately, applying this defect inspection network to the actual production line demonstrated a 72% improvement in time efficiency. This demonstrates the stability and robustness of the large, uniform defect dataset generated through the defect-background separated GAN.

关键词： Inspection Generative adversarial networks Deep learning Batteries Training Data models Production Defect detection Synthetic data detection algorithms Battery exterior defect inspection deep learning defect segmentation generative adversarial network synthetic defect

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Decoding Student Emotions: An Advanced CNN Approach for Behavior Analysis Application Using Uniform Local Binary Pattern

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IEEE ACCESS 2024年 12卷 106273-106284页

作者： Sowjanya, Uppanapalli Lakshmi Krithiga, R. Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 India

In this paper, Facial Emotion detection model, built using Uniform Local Binary Pattern, is explained using an application i.e., Student behavior detection. Student engagement is a critical factor in effective teaching and learning. Conventional approaches to evaluating student participation frequently depend on assertions made by students and observations made by teachers, which can be laborious and unreliable. The use of computer vision techniques to automatically identify the emotions and behaviors of students in real time has gained popularity in recent years. This research provides a novel computer vision-based method for detecting student behavior, emphasizing emotion recognition. We propose a system that utilizes Convolutional Neural Networks (CNNs) for emotion classification, data augmentation to enhance dataset, and Uniform Local Binary pattern (uLBP) feature extraction for improved texture analysis. The system aims to provide educators with real-time insights into student emotions, enabling them to adapt their teaching strategies accordingly. In order to expand the variety and breadth of the training dataset, we enhanced the facial expression data that we extracted from a dataset that's well suited for this work. We then applied uLBP feature extraction to capture local texture patterns in the facial images, which were then used as input to the CNN for emotion classification. According to our findings, the suggested approach successfully classified student emotions with a high accuracy of 94% and also completed the ablation study, which indicates its usefulness in practical settings.

关键词： Computer vision Deep learning Behavioral sciences Education detection algorithms deep learning emotion detection student behavior data augmentation uniform local binary pattern (uLBP) Education detection algorithms deep learning emotion detection student behavior data augmentation uniform local binary pattern (uLBP)

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FlexTrack3D: Advanced Single-Camera 3D Human Pose Tracking With FlexPoseNet and ZoeDepth

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IEEE ACCESS 2024年 12卷 171334-171347页

作者： Chen, Yingying Li, Zhitao Jiangsu Maritime Inst Sch Informat Engn Nanjing 211170 Peoples R China Chongqing Univ Sci & Technol Coll Met & Power Engn Chongqing 401331 Peoples R China Nanjing Xiaozhuang Univ Sch Informat Engn Sch Artificial Intelligence Nanjing 21171 Peoples R China

At present, the research of three-dimensional human pose tracking mainly focuses on the multi-camera system, but lacks a tracking algorithm for monocular cameras. Therefore, a 3D human pose tracking algorithm, FlexTrack3D, is proposed, which can track human 3D pose on monocular video. FlexTrack3D innovatively combines 2D human pose detection and monocular depth estimation technology. By integrating pixel coordinates of human keypoints provided by FlexPoseNet and depth data generated by ZoeDepth algorithm, FlexTrack3D can accurately model keypoints in three dimensions and accurately track their dynamic trajectories. FlexTrack3D shows the pose and trajectory of human motion data collected in indoor and outdoor environment through 3D point cloud model, which proves its accuracy and robustness. For tracking keypoints of human, a lightweight human pose detection model, FlexPoseNet, and a parameter sharing detector with multi-scale feature information are proposed, which strengthens the learning of key features on the premise of reducing the parameters of baseline model YOLO-Pose. Multi-Head Self-Attention module and Deformable Convolution Networks module are introduced into the backbone of FlexPoseNet model, which enhances the modeling ability of the network to the target's receptive field and improves the modeling flexibility of the network to the target structure. SlimNeck structure is introduced into the neck of the model, which effectively reduces the calculation and parameters of the model while maintaining the detection accuracy. In the experiment of COCO-Pose data set, the processing speed of FlexPoseNet is increased by 42.9% per second compared with the baseline model YOLO-Pose, and the detection accuracy of mAP50 and mAP50-95 is increased by 12.46% and 10.21%, which further improves the accuracy of the model on the premise of maintaining performance.

关键词： Three-dimensional displays Accuracy Feature extraction Cameras Target tracking Deep learning Computer vision Computational modeling detection algorithms Sensors deep learning depth estimation pose detection 3D pose tracking

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An Interactive Prompt Based Network for Urban Floods Area Segmentation Using UAV Images

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 935-948页

作者： Shi, Lingfei Yang, Kun Chen, Yunfang Chen, Gong Yunnan Normal Univ Fac Geog Kunming 650500 Peoples R China Western China Minist Educ China Engn Res Ctr GIS Technol Beijing 100816 Peoples R China Yunnan Key Lab Int Rivers & Transboundary Ecosecur Kunming 650500 Peoples R China

As climate change intensifies, extreme weather events like floods are occurring with increasing frequency. While data-driven deep learning methods are effective for extracting flood disaster information, their efficiency is constrained by the scarcity of postdisaster samples, the high cost of annotations, and the models' strong dependence on both the quantity and quality of data. This study introduces an interactive semantic segmentation model based on multisource UAV flood images, incorporating four types of prompts. By embedding expert knowledge into the prompt design, the model reduces annotation costs and enhances generalization capabilities. First, a prompt encoder is developed to map different types of prompt information into a three-channel space using convolutional techniques, thereby reducing sample labeling costs. Moreover, an image encoder that integrates Mamba and convolution is developed to effectively extract global spatial and channel features from flood images while minimizing computational load. Finally, a spatial and channel attention module with residual connections is introduced to enable multiscale fusion and filtering of prompt information and image features across both spatial and channel dimensions, improving the utilization of prompt information. To validate the model's performance, we conduct experiments using UAV flood imagery collected from diverse regions, backgrounds, and angles. The results demonstrate that, under consistent prompt conditions, our model extracts flood areas more efficiently, reducing misclassification and omission errors. Compared with the next best benchmark model, the intersection over union for the flood category improves by at least 3.75%.

关键词： Climate change Floods detection algorithms Prompt engineering Learning systems Semantic segmentation Autonomous aerial vehicles Interactive systems Meteorology Load modeling Feature extraction Weather forecasting Deep learning Satellite images Urban areas Flood detection prompt learning semantic segmentation UAV

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algorithms for Change detection With Sparse Signals

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2020年 68卷 1331-1345页

作者： Jain, Aditi Sarvepalli, Pradeep Bhashyam, Srikrishna Kannu, Arun Pachai Indian Inst Technol Madras Dept Elect Engn Chennai 600036 Tamil Nadu India

We consider the change detection problem where the pre-change observation vectors are purely noise and the post-change observation vectors are noise-corrupted compressive measurements of sparse signals with a common support, measured using a sensing matrix. In general, post-change probability density function (pdf) of the observations depends on parameters such as the support and variances of the sparse signal. When these parameters are unknown, we propose two approaches. In the first approach, we approximate the post-change pdf based on the known parameters such as mutual coherence of the sensing matrix and bounds on the signal variances. In the second approach, we parameterize the post-change pdf with an unknown parameter and try to estimate this parameter using two different methods, stochastic gradient descent and generalized likelihood ratio. In both these approaches, we employ the conventional cumulative sum (CUSUM) algorithm with various decision statistics such as the energy of the observations, correlation values with columns of the sensing matrix and the maximum value of such correlations. We analytically characterize the worst case detection delay and average run length to false alarm performance of pdf-approximation based approach. We also numerically study the performance and offer insights on the relevance of various decision statistics in different signal to noise ratio (SNR) regimes. We also address the problem of designing sensing matrices with small mutual coherence by using designs from quantum information theory. One such design using equi-angular lines has an additional structure which allows exact characterization of the post-change pdf of the correlation values, even when the support set of the sparse signal is unknown. We apply our detection algorithms with sensing matrix designed from equi-angular lines to a massive random access problem and show their superior performance over conventional Gold codes.

关键词： Sensors Sparse matrices Delays Change detection algorithms Probability density function detection algorithms Correlation CUSUM algorithm detection delay average run length sensing matrix design mutual coherence quantum information theory

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