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Research on Multi-level Attention-based Human Pose Estimation

Research on Multi-level Attention-based Human Pose Estimatio...

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Algorithms, High Performance computing and Artificial Intelligence (AHPCAI), international conference on

作者： Jun Gao Hua Huang QiShen Li ShaoQi Xia School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China

Detecting human key points from a single image is very challenging due to occlusion, blurring, illumination and scale changes. In this paper, this problem is addressed by designing an effective network structure. Since global and local information plays an important role in reasoning about human body structure and invisible keypoints, Multi-level Attention Network (MAN) is proposed. First, compared with traditional multi-resolution networks, it enables multi-resolution feature maps with greater information variance by generating them directly from the highest resolution feature map, which in turn increases the abundance of feature information after final fusion. Secondly, it effectively integrates global and local information in different resolution feature maps through the Feature Alignment Attention Block(FAAB), and intensifies them in a targeted manner. On the COCO dataset, with HRNet (Sun K. et al [1]) as the baseline network, HRNet of inserted MAN improves 1.1-2.3 AP points over the baseline network.

关键词： Computational modeling High performance computing Pose estimation Lighting Feature extraction Cognition Sun

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Abundant Semantic Information Module for Object Detection

Abundant Semantic Information Module for Object Detection

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Algorithms, High Performance computing and Artificial Intelligence (AHPCAI), international conference on

作者： Taida Wu Qishen Li Qiufeng Li Jun Gao School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Nondestructive Testing (Ministry of Education) Nanchang Hangkong University Nanchang Jiangxi China

Nowadays, with the high-speed iteration of convolution neural network, the efficient object detector emerges one after another. As an important branch of computer vision, object detection aims to detect where and what the object is. However, nowadays, many detector cannot extract abundant semantic information to discriminate the location and size of the objects, resulting in poor performance of the network. In this paper, a new module is proposed, named Abundant Semantic Information Module (ASIM), to enrich and expand the semantic information of the object with more and larger receptive fields. In ASIM, we blend the extracted feature maps to different degrees with different blending factors and fuse them so that all object information is given full attention. Compared to the baseline method, a wealth of experiments show that our module has achieved a significant performance improvement.

关键词： Location awareness Fuses High performance computing Semantics Neural networks Object detection Detectors

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Improper passing and lane-change related crashes: pattern recognition using association rules negative binomial mining

Improper passing and lane-change related crashes: Pattern re...

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international conference on Computational Methods and Data Engineering, ICMDE 2020

作者： Das, Subasish Chatterjee, Sudipa Mitra, Sudeshna Texas A&M Transportation Institute BryanTX77807 United States Department of Civil Engineering Indian Institute of Technology Kharagpur Kharagpur721302 India Global Road Safety Facility The World Bank 1818 H Street NW WashingtonDC20433 United States

ISBN: (纸本)9789811568756

Improper passing or lane-change related traffic crash is a critical issue. To pass or change lanes, the driver is required to make several judgments based on dynamic variables and allow little room for judgmental error that can result in drastic consequences if performed improperly. We used Florida rural roadway crash data from the second Strategic Highway Research Program (SHRP2) to investigate improper passing/lane-change related crashes. We applied an unsupervised data mining technique (known as association rules negative binomial (NB) miner) to extract the knowledge pattern of co-occurrence of the significant variables. This method identified some hidden trends from the complex nature of the traffic crash database. The findings show that this algorithm is suitable for pattern identification from traffic crash data. © Springer Nature Singapore Pte Ltd. 2021.

关键词： Data mining

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Two-Stage Semi-supervised nnU-Net Framework for Tooth Segmentation in CBCT Images

Two-Stage Semi-supervised nnU-Net Framework for Tooth Segme...

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2nd international ToothFairy2: Multi-Structure Segmentation in CBCT Volumes, ToothFairy 2024, 3D Teeth Landmarks Detection Challenge, 3DTeethLand 2024, Semi-supervised Teeth Segmentation, STS 2024 held in conjunction with the 27th international conference on Medical Image computing and Computer-Assisted Intervention, MICCAI 2024

作者： Ji, Changkai Liu, Yusheng He, Lanshan Jiang, Yuxian Huang, Chuanyi Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9783031889769

Automated tooth segmentation in Cone-Beam Computed Tomography (CBCT) images is crucial for various dental applications, including treatment planning and computer-assisted dental prosthesis design. The MICCAI STS 2024 Challenge Task 2 aims to enhance automated tooth segmentation by providing a dataset comprising both labeled and unlabeled CBCT images. This paper addresses the challenge of limited labeled data by reformulating the problem as a semi-supervised learning task. We propose a two-stage deep learning model based on nnU-Net. Our approach initially employs a low-resolution nnU-Net for quadrant segmentation, followed by a full-resolution nnU-Net for fine tooth segmentation within each quadrant. To efficiently utilize unlabeled data, we implement a selective stability-based retraining strategy to generate reliable pseudo-labels. Our method is quantitatively evaluated on the STS 2024 validation set, achieving good performance across various metrics (Dice_instance = 90.74%, Dice_image = 97.70%). The proposed approach achieved one of the highest rankings in the competition’s validation phase, demonstrating its efficacy in automatically segmenting teeth from CBCT images. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semi-supervised learning

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Activity recognition using Multi-Class Classification inside an Educational Building 18

Activity Recognition using Multi-Class Classification inside...

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IEEE international conference on Pervasive computing and Communications (PerCom)

作者： Das, Anooshmita Kjaergaard, Mikkel Baun Univ Southern Denmark Ctr Energy Informat Odense Denmark

ISBN: (纸本)9781728147161

Activity recognition can be referred to as the process of describing and classifying actions, pinpoint specific movements, and extract unique patterns from the dataset using heterogeneous sensing modalities. Activity recognition approaches have garnered the attention of researchers in the energy management domain to enhance energy utilization in buildings. In our experiment, we define activities as a combination of different actions, which are detected using multiple sensors. To learn insights for the various activities, we used inexpensive Passive Infrared (PIR) sensors in the test-bed. This study aims at gaining high-level knowledge about activities from the low-resolution sensors deployed. For accurate occupancy counts, we have used 3D Stereo Vision Cameras at the entrance, and count lines are defined to capture the transitions of inflow and outflow of multiple occupants. Multi-class labels enable activity recognition on the collected dataset. The multi-class labels used are 1) Moving, 2) Stagnant, 3) Outside, 4) Both (Moving and Stagnant), 5) No activity inside. The labeling for the multi-class is done through an algorithm using supervised learning. The data acquisition gets carried out from 23rd November to 3rd December 2018, spanning over a period for 11 days. The results document that Gradient Boosting Classifier outperforms any other Machine Learning Classification (MLC) algorithm with an accuracy of 97.59% and an F1 score of 97.40% for activity recognition. This paper also explicitly highlights the challenges and limitations faced during the initial phase for the deployment, and it identifies the key research trends and directs towards the potential improvements in the field of occupancy sensing for energy-efficient buildings.

关键词： Building Performance Occupant Behaviour Sensor Fusion pattern recognition Knowledge Discovery Machine Learning Activity recognition

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Design and Performance Analysis of Artificial Neural Network Based Artificial Synapse for Bio-inspired computing 3rd

Design and Performance Analysis of Artificial Neural Network...

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international conference on Computational Vision and Bio-Inspired computing (ICCVBIC)

作者： Prashanth, B. U., V Ahmed, Mohammed Riyaz REVA Univ Sch Elect & Commun Engn Bengaluru 560064 India

ISBN: (纸本)9783030372187;9783030372170

A variety of capacitating technologies are being surveyed for the microelectronics era. Spintronics technologies exemplify notable potential due to an abundance of features that can find applications in neuro-inspired computing. The aim is to capacitate fully resilient next-generation neuromorphic hardware and to replace platitudinous CMOS microelectronics by unique devices used in neuromorphic (Bio-Inspired) computing systems. In this research we designed, planned and initiated the study along with growing the films. Further devices are fabricated and initial device characterizations are performed. The device fabrication was supported using electron beam lithography. The paper depicts the supervised pattern recognition computing on bio-inspired platform using magnetic skyrmion-based synapses on hand written recognition digit dataset. The deep learning strategies such as Artificial Neural Network (ANN) modelling is elucidated to develop and simulate an electronic synapse. This research paves a way for a bio-inspired computing based on exotic topological spin textures.

关键词： Bio-inspired engineering pattern recognition Modelling Simulation Skyrmion Spintronics

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Examining consumers’ willingness to participate in aggregated demand response: An exploratory case study in Spain

Examining consumers’ willingness to participate in aggregat...

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IEEE international conference on Smart Grid Communications (SmartGridComm)

作者： Esther Palomar Carlos Cruz Ignacio Bravo Marina Hernandez-Grau Dept. of Computer Science University of Alcala Madrid Spain Dept. of Electronics University of Alcala Madrid Spain

ISBN: (数字)9798350318555

ISBN: (纸本)9798350318562

This paper presents a study which links electricity consumers’ technical confidence, awareness, and motivating factors affecting the adoption of demand response (DR) programmes, aggregation and other domestic energy efficiency measures. The aim is to statistically correlate these factors (and variables) to better understand their role played in the effectiveness of future design and deployment of sustainable energy community initiatives. The data for this study is collected from 75 DR pilot participants’ responses to an online categorical multiple-response survey (***/en/) carried out between November 2022 and March 2023 in Spain. We apply chi-square test of association, contingency tables as well as clustering and k-nearest neighbours algorithms to the dataset, amongst other statistical and machine learning methods, paying special attention to the data preprocessing. Despite the lack of strong associations between those variables as revealed by our analysis, it showcases a number of patterns in which factors like the consumer motivation dominate the effective participation in DR initiatives and pilots. Three conclusions can be drawn. Consumer motivation to participate in DR programmes is heterogeneous, multivariate, and non-normally distributed. The survey responses point to a flourishing of technical competence among the general consumer, though insufficient to foster proactive involvement in DR initiatives. Moreover, unsure or undecided behaviours prevail over non-cooperation, providing a window of opportunity for awareness raising strategies. To that end, we encourage strategies that relate to raise a social and community identity and awareness of the tangible benefits achieved by using more efficient technologies and engaging in new energy initiatives to motivate the change.

关键词： Surveys Accuracy Statistical analysis Electricity Clustering algorithms Predictive models Prediction algorithms Demand response Smart grids pattern recognition

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Region-Based Space Filling Curve for Medical Image Scanning

Region-Based Space Filling Curve for Medical Image Scanning

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international conference on Research in Intelligent and computing in Engineering, RICE 2020

作者： Kudabalage, Ashan Eranga Dang, Le Van Du, Leran Ueda, Yumi Sirindhorn International Institute of Technology Thammasat University Rangsit Thailand Center for Frontier Medical Engineering Chiba University Chiba Japan

ISBN: (纸本)9789811575266

A region-based space filling curve (RB) scanning method based on image segmentation and gradient vector flow (GVF) is presented. The proposed method extends the context-based space filling curve proposed in [1]. In this paper, the scanning path is guided by a preferred vector field which is constructed from the gradient vector field and enhanced by the GVF technique. The proposed method is compared with relevant scanning techniques. The study is important for image compression, pattern recognition, halftoning, and other applications in image processing. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image segmentation

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Orthogonal transformer: an efficient vision transformer backbone with token orthogonalization 22

Orthogonal transformer: an efficient vision transformer back...

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Proceedings of the 36th international conference on Neural Information Processing Systems

作者： Huaibo Huang Xiaoqiang Zhou Ran He Center for Research on Intelligent Perception and Computing CASIA Beijing China and National Laboratory of Pattern Recognition CASIA Beijing China Center for Research on Intelligent Perception and Computing CASIA Beijing China and National Laboratory of Pattern Recognition CASIA Beijing China and University of Science and Technology of China Hefei China Center for Research on Intelligent Perception and Computing CASIA Beijing China and National Laboratory of Pattern Recognition CASIA Beijing China and School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781713871088

We present a general vision transformer backbone, called as Orthogonal Transformer, in pursuit of both efficiency and effectiveness. A major challenge for vision transformer is that self-attention, as the key element in capturing long-range dependency, is very computationally expensive for dense prediction tasks (e.g., object detection). Coarse global self-attention and local self-attention are then designed to reduce the cost, but they suffer from either neglecting local correlations or hurting global modeling. We present an orthogonal self-attention mechanism to alleviate these issues. Specifically, self-attention is computed in the orthogonal space that is reversible to the spatial domain but has much lower resolution. The capabilities of learning global dependency and exploring local correlations are maintained because every orthogonal token in self-attention can attend to the entire visual tokens. Remarkably, orthogonality is realized by constructing an endoge-nously orthogonal matrix that is friendly to neural networks and can be optimized as arbitrary orthogonal matrices. We also introduce Positional MLP to incorporate position information for arbitrary input resolutions as well as enhance the capacity of MLP. Finally, we develop a hierarchical architecture for Orthogonal Transformer. Extensive experiments demonstrate its strong performance on a broad range of vision tasks, including image classification, object detection, instance segmentation and semantic segmentation.

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Annotation Performance for multi-channel time series HAR Dataset in Logistics 18

Annotation Performance for multi-channel time series HAR Dat...

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IEEE international conference on Pervasive computing and Communications (PerCom)

作者： Reining, Christopher Rueda, Fernando Moya Niemann, Friedrich Fink, Gernot A. ten Hompel, Michael TU Dortmund Univ Chair Mat Handling & Warehousing August Schmidt Str 4 D-44227 Dortmund Germany TU Dortmund Univ Pattern Recognit Embedded Syst Grp August Schmidt Str 4 D-44227 Dortmund Germany

ISBN: (纸本)9781728147161

This contribution proposes an approach for annotating human actions and their coarse semantic descriptions for multichannel time-series. For this purpose, a new dataset that consists of Optical Motion Capturing and IMU time-series data for industrial deployment is created and annotated by 6 individuals. The expenditure of time for labelling, both classes and semantic attributes, and the annotation consistency are examined. The initial annotations are revised by a single domain expert to measure its effect on the overall between-individual consistency. Consistency measurements by means of Cohen's kappa are analysed. The results give insights on the effort for dataset creation in the field of Human Activity recognition for industrial application. The Cohen's kappa for consistency assessment was moderate and substantial for the initial annotation, and it increased slightly after revision.

关键词： Human Activity recognition Labelling Annotation Human Reliability Logistics Warehousing

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