A Robust Visual Human Detection Approach With UKF-Based Motion Tracking for a Mobile Robot

Gupta, Meenakshi; Behera, Laxmidhar; Subramanian, V.; Jamshidi, Mo

doi:10.1109/jsyst.2014.2317777

Cited by 30 publications

(13 citation statements)

References 27 publications

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“…According to the NUIO given by (27) and (28), it needs to select the 2 sampling points if NUIO based on SVDCKF is used to obtain the observer gain (See (14)). Due to that only partial elements of the system state are nonlinear, if we only sample on the nonlinear part of state rather than on the entire state, it can reduce the number of sampling points so as to the amount of the filtering computation.…”

Section: Relative Theoremsmentioning

confidence: 99%

“…For example, in literature [8] the linear UIO model is expanded to NUIO to estimate the gain of the observer in which Unscented Transformation (UT) is used. Among the aforementioned filters, the CKF algorithm with its excellent performance, such as higher stability and accuracy as well as faster convergence rate than those of UKF, has been widely used in the high dimensional nonlinear system [14].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nonlinear Unknown Input Observer Based on Singular Value Decomposition Aided Reduced Dimension Cubature Kalman Filter

Zhao

Zou

et al. 2017

Mathematical Problems in Engineering

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The paper presents a nonlinear unknown input observer (NUIO) based on singular value decomposition aided reduced dimension Cubature Kalman filter (SVDRDCKF) for a special class of nonlinear systems, the nonlinearity of which is only caused by part of its states. Firstly, the algorithm of general NUIO is discussed and the unknown input observer based on singular value decomposition aided Cubature Kalman filter (SVDCKF) given. Then a special nonlinear system model with unknown input is introduced. Based on the proposed model and the corresponding NUIO, the equivalent integral form with partial sampling and all sampling of the state vector in Cubature Kalman filter is analyzed. Finally the nonlinear unknown input observer based on singular value decomposition aided reduced dimension Cubature Kalman filter is obtained. Simulation results show that the proposed algorithm can meet the requirements of the system and is more important to increase the calculating efficiency a lot, although it has a decline in the accuracy of the filter.

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Section: Relative Theoremsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nonlinear Unknown Input Observer Based on Singular Value Decomposition Aided Reduced Dimension Cubature Kalman Filter

Zhao

Zou

et al. 2017

Mathematical Problems in Engineering

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show abstract

“…Camshift algorithm firstly calculates the color probability distribution of the video image, then initializes the search window in the first frame of the video image, automatically adjusts the position and size of the search window, and realizes the tracking of the target. In the next frame video image, with the previous frame in the target object location and size as the initial search window, determine the target location and size of the new [5]. In the search window of the center of the window, when the zero-order moves 00 M , the first-order moment The zero-order moment is the sum of all pixels in the search window.…”

Section: Camshift Moving Target Tracking Algorithmmentioning

confidence: 99%

Mobile Robot Moving Target Detection and Tracking System

Zhang¹,

Zhang²,

Gao³

et al. 2017

Proceedings of the 7th International Conference on Computer Engineering and Networks — PoS(CENet2017)

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In order to solve the problem that the robot can coexist harmoniously with human beings and provide better service in the human settlement environment. Based on the Mecanum wheel mobile robot platform, by introducing the pyramid optical flow algorithm and CamShift algorithm, empirical results show that the robot can quickly predict the movement tendency and speed of the moving object according to the moving characteristics and color characteristic information of the moving object as well as accurate identification of tracking. Based on the above algorithm research, it can effectively improve the ability of robot motion planning and executing tasks in complex environment.

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“…Meenakshi Gupta et al [23] proposed a technique called background subtraction method which has a combination of both depth segmentation detector and template matching technique so as to automatically initialize the human tracking. In order to the track the human silhouette in an active surrounding, when the robot was in motion, an innovative idea of head and hand creation on the basis of depth of interest was presented in that paper.…”

Section: Recent Related Researches: a Reviewmentioning

confidence: 99%

An Innovative Moving Object Detection and Tracking System by Using Modified Region Growing Algorithm

Sujatha¹,

Kumari²

2016

SIPIJ

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A Robust Visual Human Detection Approach With UKF-Based Motion Tracking for a Mobile Robot

Cited by 30 publications

References 27 publications

Nonlinear Unknown Input Observer Based on Singular Value Decomposition Aided Reduced Dimension Cubature Kalman Filter

Nonlinear Unknown Input Observer Based on Singular Value Decomposition Aided Reduced Dimension Cubature Kalman Filter

Mobile Robot Moving Target Detection and Tracking System

An Innovative Moving Object Detection and Tracking System by Using Modified Region Growing Algorithm

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