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Human steering angle estimation in video based on key point detection and Kalman filter

Control Theory and Technology 2022年第3期20卷 408-417页

作者： Yanpeng Hu Yuxuan Liu Yanguang Xu Yinghui Wang School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China KE Holdings Inc. Beijing100085China

Human pose recognition and estimation in video is pervasive.However,the process noise and local occlusion bring great challenge to pose recognition.In this paper,we introduce the Kalman filter into pose recognition to reduce noise and solve local occlusion problem.The core of pose recognition in video is the fast detection of key points and the calculation of human steering angles.Thus,we first build a human key point detection model.Frame skipping is performed based on the Hamming distance of the hash value of every two adjacent frames in video.Noise reduction is performed on key point coordinates with the Kalman filter.To calculate the human steering angle,current state information of key points is predicted using the optimal estimation of key points at the previous time.Then human steering angle can be calculated based on current and previous state information.The improved SENet,NLNet and GCNet modules are integrated into key point detection model for improving accuracy.Tests are also given to illustrate the effectiveness of the proposed algorithm.

关键词： key point detection Part affinity fields Kalman filter Human steering angle

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Multi-Branch High-Dimensional Guided Transformer-Based 3D Human Posture Estimation

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Computers, Materials & Continua 2024年第3期78卷 3551-3564页

作者： Xianhua Li Haohao Yu Shuoyu Tian Fengtao Lin Usama Masood School of Mechanical Engineering Anhui University of TechnologyHuainan232001China School of Artificial Intelligence Anhui University of TechnologyHuainan232001China key Laboratory of Conveyance Equipment(East China Jiaotong University) Ministry of EducationNanchang330013China

The human pose paradigm is estimated using a transformer-based multi-branch multidimensional directed the three-dimensional(3D)method that takes into account self-occlusion,badly posedness,and a lack of depth data in the per-frame 3D posture estimation from two-dimensional(2D)mapping to 3D mapping.Firstly,by examining the relationship between the movements of different bones in the human body,four virtual skeletons are proposed to enhance the cyclic constraints of limb joints.Then,multiple parameters describing the skeleton are fused and projected into a high-dimensional space.Utilizing a multi-branch network,motion features between bones and overall motion features are extracted to mitigate the drift error in the estimation results.Furthermore,the estimated relative depth is projected into 3D space,and the error is calculated against real 3D data,forming a loss function along with the relative depth error.This article adopts the average joint pixel error as the primary performance metric.Compared to the benchmark approach,the estimation findings indicate an increase in average precision of 1.8 mm within the Human3.6M sample.

关键词： key point detection 3D human posture estimation computer vision deep learning

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An Intelligent Framework for Recognizing Social Human-Object Interactions

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Computers, Materials & Continua 2022年第10期73卷 1207-1223页

作者： Mohammed Alarfaj Manahil Waheed Yazeed Yasin Ghadi Tamara al Shloul Suliman A.Alsuhibany Ahmad Jalal Jeongmin Park Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Science Air UniversityIslamabad44000Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain15551UAE Department of Humanities and Social Science Al Ain UniversityAl Ain15551UAE Department of Computer Science College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Computer Engineering Tech University of KoreaSiheung-si15073Gyeonggi-doKorea

Human object interaction(HOI)recognition plays an important role in the designing of surveillance and monitoring systems for healthcare,sports,education,and public areas.It involves localizing the human and object targets and then identifying the interactions between them.However,it is a challenging task that highly depends on the extraction of robust and distinctive features from the targets and the use of fast and efficient classifiers.Hence,the proposed system offers an automated body-parts-based solution for HOI recognition.This system uses RGB(red,green,blue)images as input and segments the desired parts of the images through a segmentation technique based on the watershed algorithm.Furthermore,a convex hullbased approach for extracting key body parts has also been introduced.After identifying the key body parts,two types of features are extracted.Moreover,the entire feature vector is reduced using a dimensionality reduction technique called t-SNE(t-distributed stochastic neighbor embedding).Finally,a multinomial logistic regression classifier is utilized for identifying class labels.A large publicly available dataset,MPII(Max Planck Institute Informatics)Human Pose,has been used for system evaluation.The results prove the validity of the proposed system as it achieved 87.5%class recognition accuracy.

关键词： Dimensionality reduction human-object interaction key point detection machine learning watershed segmentation

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Multi-meter Intelligent detection and Recognition Method under Complex Background

Multi-meter Intelligent Detection and Recognition Method und...

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第三十九届中国控制会议

作者： Tao Ni Haifeng Miao Linlin Wang Shui Ni Lingtao Huang School of Mechanical and Aerospace Engineering Jilin University School of Vehicles and Energy YanShan University Office of National University Science Park Management Committee China University of Petroleum

This paper proposes a deep learning based approach for multi-meter intelligent detection and recognition under complex background. Firstly, SSD is used to detect the meter images and get the category and boxes of meters. Then the key point detection model is used to get the center point, the zero point, full-scale point and the end point of the pointer. Finally the indication is calculated through those key points. The experimental results show that the method can simultaneously identify different pointer meters, the error of key point is 2.8252 pixels and the average relative error is only 1.37%, which has high robustness and recognition accuracy.

关键词： meter intelligent detection deep learning SSD object detection key point detection

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