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Research on Human Pose Classification Based on Improved SVM

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Research on Human Pose Classification Based on Improved SVM

第33届中国控制与决策会议

作者： Yingda Zhang Hongyan Ma Yongxue Ye Jiaming Dou Jingjian Yang Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture National Virtual Simulation Experimental Center for Smart City Education Institute of Distributed Energy Storage Safety Big Data

With the development of intelligent buildings and aging of population,designing a high-accuracy alarm system is essential to prevent the elderly from accidentally falling *** address the problems of poor generalization and low accuracy of the SVM in the application of indoor elderly fall alarm systems,in this paper,the human keypoints detection algorithm,which increases the discrimination of the dataset,is proposed to pre-process the *** on this,the features of images are extracted from the histogram of the oriented gradient and the gray-level co-occurrence matrix,and the improved SVM algorithm is then used again for image *** simulation results show that the classification accuracy of using SVM to detect the dataset is around 91%,while the accuracy of the dataset classified as using improved SVM is around 97%.The method proposed in this paper is effective in increasing the accuracy of computer recognition of accidental falls by the elderly.

关键词： SVM Human Keypoints Detection Image Classification Accidental Indoor Falls Aging of Population

Enhancing Accuracy of Flexible Piezoresistive Pressure Sensors by Suppressing Seebeck Effect

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Journal of Donghua University(English Edition) 2021年第6期38卷 498-503页

作者： SUI Jingliang DU Minzhi JING Yuanyuan YANG Xiaona WAN Xuefen YANG Yi ZHANG Kun Key Laboratory of Textile Science & Technology of Ministry of Education Donghua University Shanghai 201620 China College of Textiles Donghua University Shanghai 201620 China Hebei Engineering Technology Research Center for IoT Data Acquisition & Processing North China Institute of Science and Technology Langfang 065000 China College of Computer North China Institute of Science and Technology Langfang 065000 China College of Information Science and Technology Donghua University Shanghai 201620 China

Flexible piezoresistive pressure sensors can offer convenient detection of mechanical deformations for wearable *** studies of flexible piezoresistive pressure sensors focus on the sensitivity but the low-cost and self-powered sensors remain a challenge due to the deviation of resistance signal acquisition caused by thermoelectric ***,piezoresistive pressure sensors with ultralow Seebeck coefficient of-0.72μV/K based on carbon nanotubes(CNTs)/polyethyleneimine(PEI)/melamine(CPM)sponge are *** to the diminished Seebeck effect,the CPM sponge pressure sensors successfully reduce the deviation to 18.75%and can keep stable sensitivity and resistance change under a very low working voltage and change temperature *** stable properties of the sensors make them successful to work for real-time sensing in self-powered wearable electronics.

关键词： piezoresistive pressure sensor carbon nanotube(CNT) Seebeck effect deviation in resistance measurement thermoelectric voltage

Look Inside for More: Internal Spatial Modality Perception for 3D Anomaly Detection

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arXiv 2024年

作者： Liang, Hanzhe Xie, Guoyang Hou, Chengbin Wang, Bingshu Gao, Can Wang, Jinbao College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen Audencia Financial Technology Institute Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Intelligent Manufacturing CATL Ningde China School of Computing and Artificial Intelligence Fuyao University of Science and Technology Fuzhou China School of Software Northwestern Polytechnical University Xi’an China Guangdong Provincial Key Laboratory of Intelligent Information Processing Shenzhen China

3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspired by the basic intuition of why not look inside for more, we introduce a straightforward method named Internal Spatial Modality Perception (ISMP) to explore the feature representation from internal views fully. Specifically, our proposed ISMP consists of a critical perception module, Spatial Insight Engine (SIE), which abstracts complex internal information of point clouds into essential global features. Besides, to better align structural information with point data, we propose an enhanced key point feature extraction module for amplifying spatial structure feature representation. Simultaneously, a novel feature filtering module is incorporated to reduce noise and redundant features for further aligning precise spatial structure. Extensive experiments validate the effectiveness of our proposed method, achieving object-level and pixel-level AUROC improvements of 4.2% and 13.1%, respectively, on the Real3D-AD benchmarks. Note that the strong generalization ability of SIE has been theoretically proven and is verified in both classification and segmentation tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Feature extraction

MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection

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arXiv 2024年

作者： Zhang, Yaning Wang, Tianyi Yu, Zitong Gao, Zan Shen, Linlin Chen, Shengyong Jinan250353 China Nanyang Technological University 50 Nanyang Ave Block N 4 639798 Singapore School of Computing and Information Technology Great Bay University Dongguan523000 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Jinan250014 China The Key Laboratory of Computer Vision and System Ministry of Education Tianjin University of Technology Tianjin300384 China Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518129 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia, highlighting the urgent need for robust and generalizable face forgery detection (FFD) techniques. Although existing approaches mainly capture face forgery patterns using image modality, other modalities like fine-grained noises and texts are not fully explored, which limits the generalization capability of the model. In addition, most FFD methods tend to identify facial images generated by GAN, but struggle to detect unseen diffusion-synthesized ones. To address the limitations, we aim to leverage the cutting-edge foundation model, contrastive language-image pre-training (CLIP), to achieve generalizable diffusion face forgery detection (DFFD). In this paper, we propose a novel multi-modal fine-grained CLIP (MFCLIP) model, which mines comprehensive and fine-grained forgery traces across image-noise modalities via language-guided face forgery representation learning, to facilitate the advancement of DFFD. Specifically, we devise a fine-grained language encoder (FLE) that extracts fine global language features from hierarchical text prompts. We design a multi-modal vision encoder (MVE) to capture global image forgery embeddings as well as fine-grained noise forgery patterns extracted from the richest patch, and integrate them to mine general visual forgery traces. Moreover, we build an innovative plug-and-play sample pair attention (SPA) method to emphasize relevant negative pairs and suppress irrelevant ones, allowing cross-modality sample pairs to conduct more flexible alignment. Extensive experiments and visualizations show that our model outperforms the state of the arts on different settings like cross-generator, cross-forgery, and cross-dataset evaluations. Copyright © 2024, The Authors. All rights reserved.

关键词： Alignment

Learning from partially labeled data 28

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Learning from partially labeled data

28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2020

作者： Mehrkanoon, Siamak Huang, Xiaolin Suykens, Johan A.K. Department of Data Science and Knowledge Engineering Maastricht University Netherlands Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China Department of Electrical Engineering ESAT-STADIUS KU Leuven LeuvenB-3001 Belgium

ISBN: (纸本)9782875870742

Providing sufficient labeled training data in many application domains is a laborious and costly task. Designing models that can learn from partially labeled data, or leveraging labeled data in one domain and unlabeled data in a different but related domain is of great interest in many applications. In particular, in this context one can refer to semi-supervised modelling, transfer learning, domain adaptation and multi-view learning among oth- ers. There are several possibilities for designing such models ranging from shallow to deep models. These type of models have received increasing in- terest due to their successful applications in real-life problems. This paper provides a brief overview of recent techniques in learning from partially labeled data. © ESANN 2020 - Proceedings, 28th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning.

关键词： Neural networks

Forensicability Assessment of Questioned Images in Recapturing Detection

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arXiv 2022年

作者： Chen, Changsheng Zhao, Lin Cai, Rizhao Yu, Zitong Huang, Jiwu Kot, Alex C. The Guangdong Key Laboratory of Intelligent Information Processing and Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology College of Electronics and Information Engineering Shenzhen University Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society China The Department of Electrical and Electrics Engineering Nanyang Technological University Singapore639798 Singapore

Recapture detection of face and document images is an important forensic task. With deep learning, the performances of face anti-spoofing (FAS) and recaptured document detection have been improved significantly. However, the performances are not yet satisfactory on samples with weak forensic cues. The amount of forensic cues can be quantified to allow a reliable forensic result. In this work, we propose a forensicability assessment network to quantify the forensicability of the questioned samples. The low-forensicability samples are rejected before the actual recapturing detection process to improve the efficiency of recapturing detection systems. We first extract forensicability features related to both image quality assessment and forensic tasks. By exploiting domain knowledge of the forensic application in image quality and forensic features, we define three task-specific forensicability classes and the initialized locations in the feature space. Based on the extracted features and the defined centers, we train the proposed forensic assessment network (FANet) with cross-entropy loss and update the centers with a momentum-based update method. We integrate the trained FANet with practical recapturing detection schemes in face anti-spoofing (FAS) and recaptured document detection tasks. Experimental results show that, for a generic CNN-based FAS scheme, FANet reduces the EERs from 33.75% to 19.23% under ROSE to IDIAP protocol by rejecting samples with the lowest 30% forensicability scores. The performance of FAS schemes is poor in the rejected samples, with EER as high as 56.48%. Similar performances in rejecting low-forensicability samples have been observed for the state-of-the-art approaches in FAS and recaptured document detection tasks. To the best of our knowledge, this is the first work that assesses the forensicability of recaptured document images and improves the system efficiency. Copyright © 2022, The Authors. All rights reserved.

关键词： Image quality

Camouflaged Object Detection via Context-aware Cross-level Fusion

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arXiv 2022年

作者： Chen, Geng Liu, Si-Jie Sun, Yu-Jia Ji, Ge-Peng Wu, Ya-Feng Zhou, Tao Northwestern Polytechnical University Xi’an710072 China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering China Data Processing Center School of Power and Energy China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer Science Inner Mongolia University Hohhot010021 China Artificial Intelligence Institute School of Computer Science Wuhan University Wuhan430072 China

Camouflaged object detection (COD) aims to identify the objects that conceal themselves in natural scenes. Accurate COD suffers from a number of challenges associated with low boundary contrast and the large variation of object appearances, e.g., object size and shape. To address these challenges, we propose a novel Context-aware Cross-level Fusion Network (C2F-Net), which fuses context-aware cross-level features for accurately identifying camouflaged objects. Specifically, we compute informative attention coefficients from multi-level features with our Attention-induced Cross-level Fusion Module (ACFM), which further integrates the features under the guidance of attention coefficients. We then propose a Dual-branch Global Context Module (DGCM) to refine the fused features for informative feature representations by exploiting rich global context information. Multiple ACFMs and DGCMs are integrated in a cascaded manner for generating a coarse prediction from high-level features. The coarse prediction acts as an attention map to refine the low-level features before passing them to our Camouflage Inference Module (CIM) to generate the final prediction. We perform extensive experiments on three widely used benchmark datasets and compare C2F-Net with state-of-the-art (SOTA) models. The results show that C2F-Net is an effective COD model and outperforms SOTA models remarkably. Further, an evaluation on polyp segmentation datasets demonstrates the promising potentials of our C2F-Net in COD downstream applications. Our code is publicly available at: https://***/Ben57882/C2FNet-TSCVT. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

Machine-learning interatomic potential for molecular dynamics simulation of ferroelectric KNbO3 perovskite

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arXiv 2022年

作者： Thong, Hao-Cheng Wang, XiaoYang Wang, Han Zhang, Linfeng Wang, Ke Xu, Ben State Key Laboratory of New Ceramics and Fine Processing School of Materials Science and Engineering Tsinghua University Beijing100084 China Graduate School China Academy of Engineering Physics Beijing100193 China Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Beijing Institute of Big Data Research Beijing100871 China

Ferroelectric perovskites have been ubiquitously applied in piezoelectric devices for decades, among which, eco-friendly lead-free (K,Na)NbO3-based materials have been recently demonstrated to be an excellent candidate for sustainable development. Molecular dynamics is a versatile theoretical calculation approach for the investigation of the dynamical properties of ferroelectric perovskites. However, molecular dynamics simulation of ferroelectric perovskites has been limited to simple systems, since the conventional construction of interatomic potential is rather difficult and inefficient. In the present study, we construct a machine-learning interatomic potential of KNbO3 (as a representative system of (K,Na)NbO3) by using a deep neural network model. Including first-principles calculation data into the training dataset ensures the quantum-mechanics accuracy of the interatomic potential. The molecular dynamics based on machine-learning interatomic potential shows good agreement with the first-principles calculations, which can accurately predict multiple fundamental properties, e.g., atomic force, energy, elastic properties, and phonon dispersion. In addition, the interatomic potential exhibits satisfactory performance in the simulation of domain wall and temperature-dependent phase transition. The construction of interatomic potential based on machine learning could potentially be transferred to other ferroelectric perovskites and consequently benefits the theoretical study of ferroelectrics. © 2022, CC BY.

关键词： Molecular dynamics

A Resource-Efficient Algorithm-Hardware Co-Design Towards Semi-Supervised Neurological Symptoms Prediction

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Zhao, Shiqi Li, Shanjin Wu, Di Tian, Fengshi Yang, Jie Sawan, Mohamad Northeastern University Shenyang 110819 China Northeastern University Hebei Key Laboratory of Marine Perception Network and Data Processing Hebei Province Qinhuangdao 066004 China Westlake University Center of Excellence in Biomedical Research on Advanced Integrated-on-chips Neurotechnologies (CenBRAIN Neurotech) School of Engineering Hangzhou 310024 China Westlake Institute for Advanced Study Institute of Advanced Technology Hangzhou 310024 China Hong Kong University of Science and Technology (HKUST) New Territories Hong Kong

Recent advancements in neuroscience, coupled with the rise of deep neural networks (DNNs), have markedly enhanced the ability to predict physiological disorders. Typically, these models are trained on cloud servers using labeled datasets and subsequently deployed to devices for inference. However, this conventional cloud-based training and edge inference approach is not feasible for real-life scenarios owing to issues such as data privacy, the laborious data-labeling process, and variations in conditions resulting from electrode shifts and changes in physiological status. In response to these challenges, we present resource-efficient algorithm (REAL), a resource-efficient algorithm–hardware co-design training framework. This transformer-based framework incorporates direct feedback alignment (DFA) to optimize hardware and computational efficiency during the training process. Moreover, it utilizes semi-supervised learning (SSL) along with bespoke data augmentation techniques to reduce the dependency on extensive data labeling. Based on the proposed algorithmic model, we design and implement a dedicated hardware architecture. We tested REAL on both electroencephalogram (EEG)-based seizure prediction and electrocardiogram (ECG)-based arrhythmia classification tasks. For the EEG task, our framework enhances prediction sensitivity by an average of 7.24% and decreases the false-positive rate (FPR) by 0.04/h, demonstrating robust performance across varying levels of annotated data. With minimal labeled data, REAL reached an AUC of 0.91 and a sensitivity of 89.15%, outperforming all previous works with similar task settings. For the ECG task, REAL achieved an accuracy of 82.81% and a macro-F1-score of 0.827 under a 20% labeling rate, demonstrating strong generalization to a different biomedical modality. Additionally, REAL is the first hardware design targeting semi-supervised neurological symptom prediction, achieving an energy efficiency of 3.97 TOPS/W and an area efficien

关键词： Direct feedback alignment (DFA) electroencephalogram (EEG) on-device learning semi-supervised learning (SSL)