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Kalman filter design consists of mathematical equations of two types: 1. Time Update Equations Time update equations obtain the preliminary estimation for the next state and stated covariance depending upon the current state and state covariance. Kalman filter consists of estimation of newer state by linear equations. 2. In this work, we improve the temporal relocalization method by using a network architecture that incorporates Kalman filtering (KFNet) for online camera relocalization. In particular, KFNet extends the scene coordinate regression problem to the time domain in order to recursively establish 2D and 3D correspondences for the pose determination. Moreover, compared to the state-of-the-art techniques, the proposed system yields significant improvements in recognizing facial expressions. Keywords State-space model Kalman Filter Hidden States Emotion Recognition Download conference paper PDF References Ahlberg, J.: Candide-3 - an updated parameterised face (January 2001) Google Scholar Trajectory-based event recognition is the basis of most auto-mated surveillance and monitoring applications. The events of interest for a given application domain could be a collec-tion of pre-specified events or unusual or rarely occurring events. While the former just requires specification of the events of interest as domain specific knowledge or models, the latter requires The Kalman filter model assumes the true state at time k is evolved from the state at (k − 1) according to = + + where F k is the state transition model which is applied to the previous state x k−1;; B k is the control-input model which is applied to the control vector u k;; w k is the process noise, which is assumed to be drawn from a zero mean multivariate normal distribution, , with 44 , 169-185 ( 2012) Cite this article 748 Accesses 37 Citations Metrics Abstract The ensemble Kalman filter (EnKF) is now widely used in diverse disciplines to estimate model parameters and update model states by integrating observed data. The EnKF is known to perform optimally only for multi-Gaussian distributed states and parameters. doi.org/10.1016/0168-9002 (90)91835-Y Get rights and content Abstract A progressive pattern recognition algorithm based on the Kalman filtering method has been tested. The algorithm starts from a small track segment or from a fitted track of a neighbouring detector, then extends the candidate tracks by adding measured points one by one. PDF | The ensemble Kalman filter (EnKF) is now widely used in diverse disciplines to estimate model parameters and update model states by integrating observed data. The EnKF is known to perform optimally only for multi-Gaussian distributed states and parameters. A new approach, the normal-score EnKF (NS-EnKF), has been recently proposed to handle complex aquifers with non-Gaussian distributed This paper proposed a new method to predict an elbow joint angle based on electromyography (EMG) which used a time domain feature, zero crossing, and Kalman filter. The EMG signals were collected fr