Cooperative Target Tracking by Multiagent Camera Sensor Networks via Gaussian Process

Adachi, Takashi; Hayashi, Naoki; Takai, Shigemasa

doi:10.1109/access.2022.3188857

Cited by 7 publications

(9 citation statements)

References 41 publications

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“…( 17) and known hyperparameters for outputs i = 1,...,6. This is a frequent assumption in GP based control [7,10,17] and only excludes irregularities (discontinuities) within the class eq. ( 6).…”

Section: Gp ψ Model Errormentioning

confidence: 99%

“…Due to recent advances in data-driven control, they also show great adoption in domains like motion tracking of multiple targets [5]. Moreover, in investigation of animal ecology and pest bird detterance scenarios [6], visual pursuit is a typical problem [5,7].…”

Section: Introductionmentioning

confidence: 99%

“…Motion estimators to observe target motion by cameras have been proposed in [5,7]. While they perform well in tracking the target pose, this article focuses on the passivity-based nonlinear Visual Motion Observer [8] since it can be simultaneously used to pursue targets and provides performance guarantees in the form of uniform error bounds.…”

Section: Introductionmentioning

confidence: 99%

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Visual Pursuit with Switched Motion Estimation and Rigid Body Gaussian Processes

Omainska,

Yamauchi,

Fujinami

et al. 2023

Transactions of ISCIE

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We present in this article a pursuit controller with simultaneous data-based 3D target motion prediction and switching estimation by a 2D camera when a moving target may switch between multiple motion patterns. The switching target motion is modelled by a new Gaussian Process model for rigid body motions that can predict velocity fields based on observed target motion data and an online switching estimator. We further prove that the proposed methods for motion prediction and visual pursuit ensure stability and demonstrate the increased performance of pursuing a target by a camera-equipped drone in a Digital Twin simulator.

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Section: Gp ψ Model Errormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Visual Pursuit with Switched Motion Estimation and Rigid Body Gaussian Processes

Omainska,

Yamauchi,

Fujinami

et al. 2023

Transactions of ISCIE

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show abstract

“…There are two types of monocular visual measurement of pose according to the features selected, namely measurements of cooperative targets [9][10][11] and measurements of noncooperative targets [12][13][14]. Among them, the spatial constraint relationship between target feature points in cooperative target pose measurement is controllable, which to some extent limits the application scope but reduces the difficulty of feature extraction, improves the extraction accuracy, and reduces the complexity of the pose calculation.…”

Section: Introductionmentioning

confidence: 99%

A Method for Measuring the Absolute Position and Attitude Parameters of a Moving Rigid Body Using a Monocular Camera

Guo,

Zhao,

Guo

et al. 2023

Applied Sciences

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To verify the dynamic model of a paddy-field laser grader and solve the problem of the unstable high-speed operation of the grader, a method for measuring the absolute pose parameters of a moving rigid body using a monocular camera is proposed. The proposed method does not require calibration beforehand. Using more than six non-coplanar control points symmetrically arranged in the rigid-body and world coordinate systems, the matrices of rotation and translation between the camera and two coordinate systems are obtained and the absolute pose of the rigid body is measured. In this paper, formulas of the absolute pose measurement of a moving rigid body are deduced systematically and the complete implementation is presented. Position and attitude measurement experiments carried out on a three-axis precision turntable show that the average absolute error in the attitude angle of a moving rigid body measured by an uncalibrated camera at different positions changes by no more than 0.2 degrees. An analysis of the three-dimensional coordinate errors of the centroid of a moving rigid body shows little deviation in measurements taken at the three camera positions, with the maximum deviation of the average absolute error being 0.53 cm and the maximum deviation of the standard deviation being 0.66 cm. The proposed method can measure the absolute pose of a rigid body and is insensitive to the position of the camera in the measurement process. This work thus provides guidance for the repeated measurement of the absolute pose of a moving rigid body using a monocular camera.

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“…The most common use of the information obtained from a distributed camera system is motion detection and the tracking of objects of interest [3]. Distributed camera systems have proven useful as a passive means of detecting events or situations of interest; for example, the detection of an atypical behavior [16][17][18], the evolution of the dynamics of movement of an object in time [19][20][21], target tracking for surveillance applications [3,[22][23][24][25][26][27], and tridimensional reconstruction [28][29][30]. Other important works are [16,[31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Queries through Augmented Reality for Intelligent Video Systems

Montes-Martínez,

Jiménez-Hernández,

Herrera-Navarro

et al. 2023

ASI

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Artificial vision system applications have generated significant interest as they allow information to be obtained through one or several of the cameras that can be found in daily life in many places, such as parks, avenues, squares, houses, etc. When the aim is to obtain information from large areas, it can become complicated if it is necessary to track an object of interest, such as people or vehicles, due to the vision space that a single camera can cover; this opens the way to distributed zone monitoring systems made up of a set of cameras that aim to cover a larger area. Distributed zone monitoring systems add great versatility, becoming more complex in terms of the complexity of information analysis, communication, interoperability, and heterogeneity in the interpretation of information. In the literature, the development of distributed schemes has focused on representing data communication and sharing challenges. Currently, there are no specific criteria for information exchange and analysis in a distributed system; hence, different models and architectures have been proposed. In this work, the authors present a framework to provide homogeneity in a distributed monitoring system. The information is obtained from different cameras, where a global reference system is defined for generated trajectories, which are mapped independently of the model used to obtain the dynamics of the movement of people within the vision area of a distributed system, thus allowing for its use in works where there is a large amount of information from heterogeneous sources. Furthermore, we propose a novel similarity metric that allows for information queries from heterogeneous sources. Finally, to evaluate the proposed performance, the authors developed several distributed query applications in an augmented reality system based on realistic environments and historical data retrieval using a client–server model.

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Cooperative Target Tracking by Multiagent Camera Sensor Networks via Gaussian Process

Cited by 7 publications

References 41 publications

Visual Pursuit with Switched Motion Estimation and Rigid Body Gaussian Processes

Visual Pursuit with Switched Motion Estimation and Rigid Body Gaussian Processes

A Method for Measuring the Absolute Position and Attitude Parameters of a Moving Rigid Body Using a Monocular Camera

Dynamic Queries through Augmented Reality for Intelligent Video Systems

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