The background models are crucially important for the object extraction for moving objects detection in a video.The Gaussian mixture model(GMM)is one of popular methods in the background models.Gaussian mixture model ...The background models are crucially important for the object extraction for moving objects detection in a video.The Gaussian mixture model(GMM)is one of popular methods in the background models.Gaussian mixture model which applied to the pig target detection has some shortcomings such as low efficiency of algorithm,misjudgment points and ghosts.This study proposed an improved algorithm based on adaptive Gaussian mixture model,to overcome the deficiencies of the traditional Gaussian mixture model in pig object detection.Based on Gaussian mixture background model,this paper introduced two new parameters of video frames m and T0.The Gaussian distribution was scanned once every m frames,the excessive Gaussian distribution was deleted to improve the convergence speed of the model.Meanwhile,using different learning rates to suppress ghosts,a higher decreasing learning rate was adopted to accelerate the background modeling before T_(0),the background model would become stable as the time continued and a smaller learning rate could be used.In order to maintain a stable background and reduce noise interference,a fixed learning rate after T_(0) was used.Results of experiments indicated that this algorithm could quickly build the initial background model,detect the moving target pigs,and extract the complete contours of the target pigs’.The algorithm is characterized by good robustness and adaptability.展开更多
This paper addresses an uncertain nonlinear control system problem with complex state constraints and mismatched uncertainties.A novel Gaussian Mixture Model(GMM)based adaptive PID-Nonsingular Terminal Sliding Mode Co...This paper addresses an uncertain nonlinear control system problem with complex state constraints and mismatched uncertainties.A novel Gaussian Mixture Model(GMM)based adaptive PID-Nonsingular Terminal Sliding Mode Control(NTSMC)(GMM-adaptive-PID-NTSMC)method is proposed.It is achieved by combining a GMM based adaptive potential function with a novel switching surface of PID-NTSMC.Next,the stability of the closed-loop system is proved.The main contribution of this paper is that the GMM method is applied to obtain the analytic description of the complex bounded state constraints,ensuring that the states'constraints are not violated with GMM-based adaptive potential function.The developed potential function can consider the influence of uncertainties.More importantly,the GMM-adaptive-PID-NTSMC can be generalized to control a more representative class of uncertain nonlinear systems with constrained states and mismatched uncertainties.In addition,the proposed controller enhances the robustness,and requires less control cost and reduces the steady state error with respect to the Artificial Potential Function based Nonsingular Terminal Sliding Mode Control(APF-NTSMC),GMM-NTSMC and GMM-adaptive-NTSMC.At last,numerical simulation is performed to validate the superior performance of the proposed controller.展开更多
基金supported by the National High Technology Research and Development Program of China(2013AA102306)Independent Innovation Capability of Shandong Province(2014XGA13054).
文摘The background models are crucially important for the object extraction for moving objects detection in a video.The Gaussian mixture model(GMM)is one of popular methods in the background models.Gaussian mixture model which applied to the pig target detection has some shortcomings such as low efficiency of algorithm,misjudgment points and ghosts.This study proposed an improved algorithm based on adaptive Gaussian mixture model,to overcome the deficiencies of the traditional Gaussian mixture model in pig object detection.Based on Gaussian mixture background model,this paper introduced two new parameters of video frames m and T0.The Gaussian distribution was scanned once every m frames,the excessive Gaussian distribution was deleted to improve the convergence speed of the model.Meanwhile,using different learning rates to suppress ghosts,a higher decreasing learning rate was adopted to accelerate the background modeling before T_(0),the background model would become stable as the time continued and a smaller learning rate could be used.In order to maintain a stable background and reduce noise interference,a fixed learning rate after T_(0) was used.Results of experiments indicated that this algorithm could quickly build the initial background model,detect the moving target pigs,and extract the complete contours of the target pigs’.The algorithm is characterized by good robustness and adaptability.
基金supported by the National Natural Science Foundation of China(Nos.61690210,61690213,12002383)。
文摘This paper addresses an uncertain nonlinear control system problem with complex state constraints and mismatched uncertainties.A novel Gaussian Mixture Model(GMM)based adaptive PID-Nonsingular Terminal Sliding Mode Control(NTSMC)(GMM-adaptive-PID-NTSMC)method is proposed.It is achieved by combining a GMM based adaptive potential function with a novel switching surface of PID-NTSMC.Next,the stability of the closed-loop system is proved.The main contribution of this paper is that the GMM method is applied to obtain the analytic description of the complex bounded state constraints,ensuring that the states'constraints are not violated with GMM-based adaptive potential function.The developed potential function can consider the influence of uncertainties.More importantly,the GMM-adaptive-PID-NTSMC can be generalized to control a more representative class of uncertain nonlinear systems with constrained states and mismatched uncertainties.In addition,the proposed controller enhances the robustness,and requires less control cost and reduces the steady state error with respect to the Artificial Potential Function based Nonsingular Terminal Sliding Mode Control(APF-NTSMC),GMM-NTSMC and GMM-adaptive-NTSMC.At last,numerical simulation is performed to validate the superior performance of the proposed controller.
