Contribution of poses screen preimpregnated(PSP) installed at openings and eaves of dwellings in the reduction of malaria transmission in the commune of agugus in bnin

Objective:To evaluate the contribution of poses screen pre-impregnated(PSP) installed at openings and eaves of dwellings in the reduction of malaria transmission in the commune of Aguegues in Benin.Methods:The PSP were manufactured from preimpregnated Olyset Net.They were installed at windows,eaves and doors of 70 dwellings.320 children aged 6-59 months were treated and 311 children were recruited in the control zone.Variables measured are:plasmodic index(IP),gametoeyte index,parasite density(PD),fever,hemoglobin,anemia. Results:The global IP was 16.62%with PSP and 72.20%without PSP.Gametoeyte index did not differ significantly between the treated zone(27.8) and the control zone(29.1).The total geometric mean of DP was 309 in the treated zone and 600 in the control zone.Hemoglobin level is 8.7 in the control zone and 9.5 in the treated zone.We noted a predominance of anemia in the control zone compared to the treated zone.Conclusions:The PSP have contributed to a significant reduction in morbidity in the commune of Aguegues.

Gaddafi’s Death Poses Challenges

The death of Muammar Gaddafi marks a new era for Libya.It also poses a huge challenge for Libyan authorities dealing with tribal conflicts.He Wenping, a researcher with the Institute of West-Asian and African Studies at the Chinese Academy of Social Sciences, believes that Libya is in danger of falling into a period of internal strife and tribal conflict.Her thoughts are as follows:

Effects of Dressing Poses on Clothing Thermal Insulation

With a thermal manikin, the effects of dressing poses on clothing thermal insulation are studied. It is found that the thermal insulation of still air layer over human body has not been influenced by the dressing poses, but the dressing poses have effects on the thermal insulation of clothing system.

Tracking Human Poses with Head Orientation Estimation

Lots of progress has been made recently on 2 D human pose tracking with tracking-by-detection approaches. However,several challenges still remain in this area which is due to self-occlusions and the confusion between the left and right limbs during tracking. In this work,a head orientation detection step is introduced into the tracking framework to serve as a complementary tool to assist human pose estimation. With the face orientation determined,the system can decide whether the left or right side of the human body is exactly visible and infer the state of the symmetric counterpart. By granting a higher priority for the completely visible side,the system can avoid double counting to a great extent when inferring body poses. The proposed framework is evaluated on the HumanEva dataset. The results show that it largely reduces the occurrence of double counting and distinguishes the left and right sides consistently.

Waves of 3D Marine Structures Slamming at Different Initial Poses in Complex Wind-Wave-Flow Environments

Aimed at the hydrodynamic response for marine structures slamming into water, based on the mechanism analysis to the slamming process, and by combining 3D N-S equation and k-ε turbulent kinetic equation with structure fully 6DOF motion equation, a mathematical model for the wind-fluid-solid interaction is established in 3D marine structure slamming wave at free poses and wind-wave-flow complex environments. Compared with the results of physical model test, the numerical results from the slamming wave well correspond with the experimental results. Through the mathematical model, the wave-making issue of 3D marine structure at initial pose falls into water in different complex wind, wave and flow environments is investigated. The research results show that various kinds of natural factors and structure initial poses have different influence on the slamming wave, and there is an obvious rule in this process.

Robust facial expression recognition system in higher poses

Facial expression recognition(FER)has numerous applications in computer security,neuroscience,psychology,and engineering.Owing to its non-intrusiveness,it is considered a useful technology for combating crime.However,FER is plagued with several challenges,the most serious of which is its poor prediction accuracy in severe head poses.The aim of this study,therefore,is to improve the recognition accuracy in severe head poses by proposing a robust 3D head-tracking algorithm based on an ellipsoidal model,advanced ensemble of AdaBoost,and saturated vector machine(SVM).The FER features are tracked from one frame to the next using the ellipsoidal tracking model,and the visible expressive facial key points are extracted using Gabor filters.The ensemble algorithm(Ada-AdaSVM)is then used for feature selection and classification.The proposed technique is evaluated using the Bosphorus,BU-3DFE,MMI,CK^(+),and BP4D-Spontaneous facial expression databases.The overall performance is outstanding.

Eye location under different eye poses,scales,and illuminations

Robust non-intrusive eye location plays an important role in vision-based man-mechine interaction. A modified Hausdorff distance based measure to localize the eyes is proposed, which could tolerate various changes in eye pose, shape, and scale. To eliminate the effects of the illumination variations, an 8- neighbour-based transformation of the gray images is proposed. The transformed image is less sensitive to illumination changes while preserves the appearance information of eyes. All the localized candidates of eyes are identified by back-propagation neural networks. Experiments demonstrate that the robust method for eye location is able to localize eyes with different eye sizes, shapes, and poses under different illuminations.

基于MediaPipe Pose的人体动作识别方法研究

针对已有人体动作识别方法存在识别效率低、检测速度慢等问题,提出了基于MediaPipe Pose算法的人体动作识别方法。具体内容:将摄像头实时采集数据输入到检测网络以获取人体33个关键点的坐标信息,然后通过关键点的空间位置组合来确定人体动作类别;采用COCO数据集格式标定动作类别,并且对动作标签进行onehot编码,训练人体动作识别模型;利用单目RGB摄像头对8类动作进行实验验证。结果表明,基于MediaPipe Pose算法的人体动作识别方法其帧率达到30帧/s,识别精确率为96.67%,能够实时、准确地识别人体动作。

一种轻量化的排球自垒姿态检测算法

边缘设备有限的存储和处理能力,在实际应用中难以部署较为复杂的YOLOv7pose姿态检测模型。对YOLOv7pose进行了一系列轻量化处理,使用FasterNet的主干网络重构YOLOv7pose的特征提取网络,将特征提取后的输出应用CBAM注意力机制来弥补精度上的损失,最后对冗余的多尺度检测头进行删减,实验表明改进后的轻量化网络较原网络的参数量下降了2/3,计算速度提升了2.5倍,精度仅减少了3.8%,能够满足边缘设备实时检测排球对墙自垒过程中的人体姿态情况。

Construction Activity Analysis of Workers Based on Human Posture Estimation Information

Identifying workers’construction activities or behaviors can enable managers to better monitor labor efficiency and construction progress.However,current activity analysis methods for construction workers rely solely on manual observations and recordings,which consumes considerable time and has high labor costs.Researchers have focused on monitoring on-site construction activities of workers.However,when multiple workers are working together,current research cannot accu rately and automatically identify the construction activity.This research proposes a deep learning framework for the automated analysis of the construction activities of multiple workers.In this framework,multiple deep neural network models are designed and used to complete worker key point extraction,worker tracking,and worker construction activity analysis.The designed framework was tested at an actual construction site,and activity recognition for multiple workers was performed,indicating the feasibility of the framework for the automated monitoring of work efficiency.

Lightweight Multi-Resolution Network for Human Pose Estimation

Human pose estimation aims to localize the body joints from image or video data.With the development of deeplearning,pose estimation has become a hot research topic in the field of computer vision.In recent years,humanpose estimation has achieved great success in multiple fields such as animation and sports.However,to obtainaccurate positioning results,existing methods may suffer from large model sizes,a high number of parameters,and increased complexity,leading to high computing costs.In this paper,we propose a new lightweight featureencoder to construct a high-resolution network that reduces the number of parameters and lowers the computingcost.We also introduced a semantic enhancement module that improves global feature extraction and networkperformance by combining channel and spatial dimensions.Furthermore,we propose a dense connected spatialpyramid pooling module to compensate for the decrease in image resolution and information loss in the network.Finally,ourmethod effectively reduces the number of parameters and complexitywhile ensuring high performance.Extensive experiments show that our method achieves a competitive performance while dramatically reducing thenumber of parameters,and operational complexity.Specifically,our method can obtain 89.9%AP score on MPIIVAL,while the number of parameters and the complexity of operations were reduced by 41%and 36%,respectively.

Investigation of Inside-Out Tracking Methods for Six Degrees of Freedom Pose Estimation of a Smartphone in Augmented Reality

Six degrees of freedom(6DoF)input interfaces are essential formanipulating virtual objects through translation or rotation in three-dimensional(3D)space.A traditional outside-in tracking controller requires the installation of expensive hardware in advance.While inside-out tracking controllers have been proposed,they often suffer from limitations such as interaction limited to the tracking range of the sensor(e.g.,a sensor on the head-mounted display(HMD))or the need for pose value modification to function as an input interface(e.g.,a sensor on the controller).This study investigates 6DoF pose estimation methods without restricting the tracking range,using a smartphone as a controller in augmented reality(AR)environments.Our approach involves proposing methods for estimating the initial pose of the controller and correcting the pose using an inside-out tracking approach.In addition,seven pose estimation algorithms were presented as candidates depending on the tracking range of the device sensor,the tracking method(e.g.,marker recognition,visual-inertial odometry(VIO)),and whether modification of the initial pose is necessary.Through two experiments(discrete and continuous data),the performance of the algorithms was evaluated.The results demonstrate enhanced final pose accuracy achieved by correcting the initial pose.Furthermore,the importance of selecting the tracking algorithm based on the tracking range of the devices and the actual input value of the 3D interaction was emphasized.

Design of a Lightweight Compressed Video Stream-Based Patient Activity Monitoring System

Inpatient falls from beds in hospitals are a common problem.Such falls may result in severe injuries.This problem can be addressed by continuous monitoring of patients using cameras.Recent advancements in deep learning-based video analytics have made this task of fall detection more effective and efficient.Along with fall detection,monitoring of different activities of the patients is also of significant concern to assess the improvement in their health.High computation-intensive models are required to monitor every action of the patient precisely.This requirement limits the applicability of such networks.Hence,to keep the model lightweight,the already designed fall detection networks can be extended to monitor the general activities of the patients along with the fall detection.Motivated by the same notion,we propose a novel,lightweight,and efficient patient activity monitoring system that broadly classifies the patients’activities into fall,activity,and rest classes based on their poses.The whole network comprises three sub-networks,namely a Convolutional Neural Networks(CNN)based video compression network,a Lightweight Pose Network(LPN)and a Residual Network(ResNet)Mixer block-based activity recognition network.The compression network compresses the video streams using deep learning networks for efficient storage and retrieval;after that,LPN estimates human poses.Finally,the activity recognition network classifies the patients’activities based on their poses.The proposed system shows an overall accuracy of approx.99.7% over a standard dataset with 99.63% fall detection accuracy and efficiently monitors different events,which may help monitor the falls and improve the inpatients’health.

Abnormal Action Recognition with Lightweight Pose Estimation Network in Electric Power Training Scene

Electric power training is essential for ensuring the safety and reliability of the system.In this study,we introduce a novel Abnormal Action Recognition(AAR)system that utilizes a Lightweight Pose Estimation Network(LPEN)to efficiently and effectively detect abnormal fall-down and trespass incidents in electric power training scenarios.The LPEN network,comprising three stages—MobileNet,Initial Stage,and Refinement Stage—is employed to swiftly extract image features,detect human key points,and refine them for accurate analysis.Subsequently,a Pose-aware Action Analysis Module(PAAM)captures the positional coordinates of human skeletal points in each frame.Finally,an Abnormal Action Inference Module(AAIM)evaluates whether abnormal fall-down or unauthorized trespass behavior is occurring.For fall-down recognition,three criteria—falling speed,main angles of skeletal points,and the person’s bounding box—are considered.To identify unauthorized trespass,emphasis is placed on the position of the ankles.Extensive experiments validate the effectiveness and efficiency of the proposed system in ensuring the safety and reliability of electric power training.

Movement Function Assessment Based on Human Pose Estimation from Multi-View

Human pose estimation is a basic and critical task in the field of computer vision that involves determining the position(or spatial coordinates)of the joints of the human body in a given image or video.It is widely used in motion analysis,medical evaluation,and behavior monitoring.In this paper,the authors propose a method for multi-view human pose estimation.Two image sensors were placed orthogonally with respect to each other to capture the pose of the subject as they moved,and this yielded accurate and comprehensive results of three-dimensional(3D)motion reconstruction that helped capture their multi-directional poses.Following this,we propose a method based on 3D pose estimation to assess the similarity of the features of motion of patients with motor dysfunction by comparing differences between their range of motion and that of normal subjects.We converted these differences into Fugl–Meyer assessment(FMA)scores in order to quantify them.Finally,we implemented the proposed method in the Unity framework,and built a Virtual Reality platform that provides users with human–computer interaction to make the task more enjoyable for them and ensure their active participation in the assessment process.The goal is to provide a suitable means of assessing movement disorders without requiring the immediate supervision of a physician.

Machine learning algorithm partially reconfigured on FPGA for an image edge detection system

Unmanned aerial vehicles(UAVs)have been widely used in military,medical,wireless communications,aerial surveillance,etc.One key topic involving UAVs is pose estimation in autonomous navigation.A standard procedure for this process is to combine inertial navigation system sensor information with the global navigation satellite system(GNSS)signal.However,some factors can interfere with the GNSS signal,such as ionospheric scintillation,jamming,or spoofing.One alternative method to avoid using the GNSS signal is to apply an image processing approach by matching UAV images with georeferenced images.But a high effort is required for image edge extraction.Here a support vector regression(SVR)model is proposed to reduce this computational load and processing time.The dynamic partial reconfiguration(DPR)of part of the SVR datapath is implemented to accelerate the process,reduce the area,and analyze its granularity by increasing the grain size of the reconfigurable region.Results show that the implementation in hardware is 68 times faster than that in software.This architecture with DPR also facilitates the low power consumption of 4 mW,leading to a reduction of 57%than that without DPR.This is also the lowest power consumption in current machine learning hardware implementations.Besides,the circuitry area is 41 times smaller.SVR with Gaussian kernel shows a success rate of 99.18%and minimum square error of 0.0146 for testing with the planning trajectory.This system is useful for adaptive applications where the user/designer can modify/reconfigure the hardware layout during its application,thus contributing to lower power consumption,smaller hardware area,and shorter execution time.

Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

基于OpenPose的晕眩警告设计

文章采用了Open Pose识别人体摔倒姿态特征,提取人体关键性的关节节点。鉴于摔倒和站立的人体姿态有着明显的区别,仅仅认识到在地上就判定摔倒是不准确的,还需要结合站立动作特征。因此,识别两种动作特征后,可发现可能晕眩并及时通知保卫室,从而有效降低因晕眩可能导致无人察觉的安全隐患,确保人身安全。

Overview of 3D Human Pose Estimation

3D human pose estimation is a major focus area in the field of computer vision,which plays an important role in practical applications.This article summarizes the framework and research progress related to the estimation of monocular RGB images and videos.An overall perspective ofmethods integrated with deep learning is introduced.Novel image-based and video-based inputs are proposed as the analysis framework.From this viewpoint,common problems are discussed.The diversity of human postures usually leads to problems such as occlusion and ambiguity,and the lack of training datasets often results in poor generalization ability of the model.Regression methods are crucial for solving such problems.Considering image-based input,the multi-view method is commonly used to solve occlusion problems.Here,the multi-view method is analyzed comprehensively.By referring to video-based input,the human prior knowledge of restricted motion is used to predict human postures.In addition,structural constraints are widely used as prior knowledge.Furthermore,weakly supervised learningmethods are studied and discussed for these two types of inputs to improve the model generalization ability.The problem of insufficient training datasets must also be considered,especially because 3D datasets are usually biased and limited.Finally,emerging and popular datasets and evaluation indicators are discussed.The characteristics of the datasets and the relationships of the indicators are explained and highlighted.Thus,this article can be useful and instructive for researchers who are lacking in experience and find this field confusing.In addition,by providing an overview of 3D human pose estimation,this article sorts and refines recent studies on 3D human pose estimation.It describes kernel problems and common useful methods,and discusses the scope for further research.

Squirrel Search Optimization with Deep Convolutional Neural Network for Human Pose Estimation

Human pose estimation(HPE)is a procedure for determining the structure of the body pose and it is considered a challenging issue in the computer vision(CV)communities.HPE finds its applications in several fields namely activity recognition and human-computer interface.Despite the benefits of HPE,it is still a challenging process due to the variations in visual appearances,lighting,occlusions,dimensionality,etc.To resolve these issues,this paper presents a squirrel search optimization with a deep convolutional neural network for HPE(SSDCNN-HPE)technique.The major intention of the SSDCNN-HPE technique is to identify the human pose accurately and efficiently.Primarily,the video frame conversion process is performed and pre-processing takes place via bilateral filtering-based noise removal process.Then,the EfficientNet model is applied to identify the body points of a person with no problem constraints.Besides,the hyperparameter tuning of the EfficientNet model takes place by the use of the squirrel search algorithm(SSA).In the final stage,the multiclass support vector machine(M-SVM)technique was utilized for the identification and classification of human poses.The design of bilateral filtering followed by SSA based EfficientNetmodel for HPE depicts the novelty of the work.To demonstrate the enhanced outcomes of the SSDCNN-HPE approach,a series of simulations are executed.The experimental results reported the betterment of the SSDCNN-HPE system over the recent existing techniques in terms of different measures.