This paper proposes a simulation approach to detect different human poses in real time with streaming data. Pose detection in real time is a critical area for many of the applications in different domains where the available literature deals with training-based models on huge amount of data and the methods use 3D cameras for accurate predictions. It also requires huge computational efforts and GPU machines to obtain different human poses in real time. The available methods in the literature are with high frame rate requirement and use previous frames as well for predicting the poses of human. If the frame rate is less, then the methods fail to predict the poses accurately and efficiently. The proposed simulation mechanism describes the simulation of different poses of human and generates feature descriptors for each of the pose and trains the model using simple classifier. The trained model predicts the real-time human pose detection on video streaming data. The different poses are predicted with less frame rate using simple 2D cameras and with accurate predictions by reducing the processing time and with less computational efforts. The proposed solution will be used to predict the candidate poses or gestures in the virtual interview application.
This paper presents a comparative study of the performance of different Monte Carlo Simulation methods in the computation of rare event probabilities in Reliability Theory. We evaluate the performance of 4 well known Markov Chain Monte Carlo methods (MCMC), namely Metropolis-Hasting (MH), Hamiltonian or Hybrid Monte Carlo (HYBRID), Delayed Rejection and Adaptive Metropolis (DRAM), and Differential Evolution Adaptive Metropolis (DREAM), for computing the Probability of Failure using the Reliability Theory framework. We also compared the results of simulations with an approximate analytical method called First Order Reliability Method (FORM). The study shows that while both HYBRID and DREAM produce more accurate results, contrary to intuition, HYBRID method was very slow in performance.
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