Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Human Pose Estimation Benchmarking And Action Recognition


Affiliations
1 Graduated Student, JNTUH Hyderabad, Telegana, India
     

   Subscribe/Renew Journal


Existing frameworks for video-based posture assessment and following battle to perform well on reasonable recordings with various individuals and regularly neglect to yield body-present directions steady over the long haul. To address this inadequacy this paper presents Pose Track which is another huge scope benchmark for video-based human posture assessment and verbalized following. Our new benchmark includes three assignments zeroing in on I) single-outline multi-individual posture assessment, ii) multi-individual posture assessment in recordings, and iii) multi-individual enunciated following. To set up the benchmark, we gather, explain and discharge another dataset that highlights recordings with various individuals marked with individual tracks and verbalized posture. A public brought together assessment worker is given to permit the examination local area to assess on a held-out test set. Moreover, we lead a broad trial concentrate on ongoing methodologies to explained present following and give examination of the qualities and shortcomings of the cutting edge. We imagine that the proposed benchmark will invigorate useful examination both by giving a huge and agent preparing dataset just as giving a stage to impartially assess and think about the proposed strategies

Keywords

Benchmark, Dataset, Multi-Person, Pose Track, MPII, COCO.
User
Subscription Login to verify subscription
Notifications
Font Size

  • Eklas Hossain, (Senior Member, Ieee), Imtiaj Khan, Fuad Un-Noor, ―Application Of Big Data And Machine Learning In Smart Grid, And Associated Security Concerns: A Review‖, Received December 18, 2018, Accepted January 8, 2019, Date Of Publication January 24, 2019, Date Of Current Version February 8, 2019. Digital Object Identifier 10.1109/Access.2019.2894819
  • Ricardo Marquez, Carlos F.M. Coimbra, ―Forecasting Of Global And Direct Solar Irradiance Using Stochastic Learning Methods, Ground Experiments And The Nws Database‖, Solar Energy 85 (2011) 746–756.
  • Henrik Lund A, Willett Kempton, ―Integration Of Renewable Energy Into The Transport And Electricity Sectors Through V2g‖, Energy Policies, January 2011
  • Bo Zhu, Min-You Chen, Neal Wade, Li Ran, ―A Prediction Model For Wind Farm Power Generation Based On Fuzzy Modelling‖, 2011 International Conference On Environmental Science And Engineering (Icese2011)
  • J.L. Torres A, A. Garcı´A, M. De Blas, A. De Francisco, ―Forecast Of Hourly Average Wind Speed With Arma Models In Navarre (Spain)‖, Solar Energy 79 (2005) 65–77
  • M. Carolin Mabel_, E. Fernandez1, ―Analysis Of Wind Power Generation And Prediction Using Ann:A Case Study‖, Renewable Energy 33 (2008) 986–992
  • S Huhui Li, Donald C. Wunsch, Edgar A. O’hair, And Michael G. Giesselmann, ―Using Neural Networks To Estimate Wind Turbine Power Generation‖, IEEE Transactions On Energy Conversion, Vol. 16, No. 3, September 2001
  • Anurag More,M.C. Deo, ―Forecasting Wind With Neural Networks‖, Marine Structures 16 (2003) 35–49
  • R.E. Abdel-Aal, M.A. Elhadidy, S.M. Shaahid, ―Modeling And Forecasting The Mean Hourly Wind Speed Time Series Using Gmdh- Based Abductive Networks‖, Renewable Energy 34 (2009) 1686–1699
  • Chiou-Jye Huang, Ping-Huan Kuo, ―A Short-Term Wind Speed Forecasting Model By Using Artificial Neural Networks With Stochastic Optimization For Renewable Energy Systems‖, Mdpi Energies
  • A. Sfetsos, ―A Novel Approach For The Forecasting Of Mean Hourly Wind Speed Time Series‖, October 2001, Renewable Energy 27 (2002) 163–174
  • Saeed Samadianfar, Sajjad Hashemi, Katayoun Kargar, Mojtaba Izadyar, Ali Mostafaeipour, Amir Mosavi, Narjes Nabipour, Shahaboddin Shamshirband, ―Wind Speed Prediction Using A Hybrid Model Of The Multi-Layer Perceptron And Whale Optimization Algorithm‖, Energy Reports 6 (2020) 1147–1159
  • Jikai Duan, Hongchao Zuocp, Yulong Bai, Jizheng Duan, Mingheng Chang, Bolong Chen, ―Short-Term Wind Speed Forecasting Using Recurrent Neural Networks With Error Correction‖, Energy.2020.119397.
  • Fitriana R. Ningsih, Esmeralda C. Djamal, Asep Najmurrakhman, ―Wind Speed Forecasting Using Recurrent Neural Networks And Long Short Term Memory‖, 2019 6th International Conference On Instrumentation, Control, And Automation (Ica) Bandung, Indonesia. 31 July – 2 August 2019.
  • Noman Shabbir, Lauri Kütt, Roya Amadiahanger, Muhammad N. Iqbal, ―Wind Energy Forecasting Using Recurrent Neural Networks‖, Proceedings Of The 2019 Ieee Conference On Big Data, Knowledge And Control Systems Engineering (Bdkcse)
  • Fitriana R. Ningsih, Esmeralda C. Djamal, Asep Najmurrakhman, ―Wind Speed Forecasting Using Recurrent Neural Networks And Long Short Term Memor‖, 2019 6th International Conference On Instrumentation, Control, And Automation (Ica) Bandung, Indonesia. 31 July – 2 August 2019
  • M. Madhiarasan, ―Accurate Prediction Of Different Forecast Horizons Wind Speed Using A Recursive Radial Basis Function Neural Network‖, Protection And Control Of Modern Power Systems (2020)
  • Zhao-Sui Zhang, Yuan-Zhangsun, Lincheng, ―Potential Of Trading Wind Power As Regulation Services In The California Short-Term Electricity Market‖, Energy Policy59(2013)885–897
  • H.Z. Wang, G.B. Wang, G.Q. Li, J.C. Peng, Y.T. Liu, ―Deep Belief Network Based Deterministic And Probabilistic Wind Speed Forecasting Approach‖, Applied Energy 182 (2016) 80–93
  • Jianzhou Wang, Jianming Hu, ―A Robust Combination Approach For Short-Term Wind Speed Forecasting And Analysis E Combination Of The Arima (Autoregressive Integrated Moving Average), Elm (Extreme Learning Machine), Svm (Support Vector Machine) And Lssvm (Least Square Svm) Forecasts Using A Gpr(Gaussian Process Regression) Model‖, Energy 93 (2015) 41-56
  • Lin Ye, Yongning Zhao, Cheng Zeng, Cihang Zhang, ―Short-Term Wind Power Prediction Based On Spatial Model‖, Renewable Energy 101 (2017) 1067e1074
  • Wen-Yeau Chang, ―A Literature Review Of Wind Forecasting Methods‖, Journal Of Power And Energy Engineering, 2014, 2, 161-168
  • Ying Denga, Bofu Wanga, Zhiming Lua, ―A Hybrid Model Based On Data Preprocessing Strategy And Error Correction System For Wind Speed Forecasting‖, Energy Conversion And Management 212 (2020) 112779
  • G.W. Chang, H.J. Lu, Y.R. Chang, Y.D. Lee, ―An Improved Neural Network-Based Approach For Short-Term Wind Speed And Power Forecast‖, Renewable Energy 105 (2017) 301e311
  • G. Santamaría-Bonfil, A. Reyes-Ballesteros, C. Gershenson, ―Wind Speed Forecasting For Wind Farms: A Method Based On Support Vector Regression‖, Renewable Energy 85 (2016) 790-809
  • Neelesh Sharma And Ravinesh Deo, ―Wind Speed Forecasting In Nepal Using Self-Organizing Map-Based Online Sequential Extreme Learning Machine‖, Predictive Modelling For Energy Management And Power Systems Engineering, Chapter 4.
  • Lian Tan , Jing Han , And Hongtao Zhang, ―Ultra-Short-Term Wind Power Prediction By Salp Swarm Algorithm-Based Optimizing Extreme Learning Machine‖, Ieee Power & Energy Society Section, March 13, 2020.
  • Cen Chen, ―Extreme Learning Machine And Its Applications In Big Data Processing‖, Big Data Analytics For Sensor-Network Collected Intelligence, 2017

Abstract Views: 181

PDF Views: 0




  • Human Pose Estimation Benchmarking And Action Recognition

Abstract Views: 181  |  PDF Views: 0

Authors

Vineethapodila
Graduated Student, JNTUH Hyderabad, Telegana, India

Abstract


Existing frameworks for video-based posture assessment and following battle to perform well on reasonable recordings with various individuals and regularly neglect to yield body-present directions steady over the long haul. To address this inadequacy this paper presents Pose Track which is another huge scope benchmark for video-based human posture assessment and verbalized following. Our new benchmark includes three assignments zeroing in on I) single-outline multi-individual posture assessment, ii) multi-individual posture assessment in recordings, and iii) multi-individual enunciated following. To set up the benchmark, we gather, explain and discharge another dataset that highlights recordings with various individuals marked with individual tracks and verbalized posture. A public brought together assessment worker is given to permit the examination local area to assess on a held-out test set. Moreover, we lead a broad trial concentrate on ongoing methodologies to explained present following and give examination of the qualities and shortcomings of the cutting edge. We imagine that the proposed benchmark will invigorate useful examination both by giving a huge and agent preparing dataset just as giving a stage to impartially assess and think about the proposed strategies

Keywords


Benchmark, Dataset, Multi-Person, Pose Track, MPII, COCO.

References