Estimating the motion of a maneuvering target with time-sequentially sampled imagery (original) (raw)

Adaptive Smoothing for Trajectory Reconstruction

arXiv (Cornell University), 2018

Trajectory reconstruction is the process of inferring the path of a moving object between successive observations. In this paper, we propose a smoothing spline -- which we name the V-spline -- that incorporates position and velocity information and a penalty term that controls acceleration. We introduce a particular adaptive V-spline designed to control the impact of irregularly sampled observations and noisy velocity measurements. A cross-validation scheme for estimating the V-spline parameters is given and we detail the performance of the V-spline on four particularly challenging test datasets. Finally, an application of the V-spline to vehicle trajectory reconstruction in two dimensions is given, in which the penalty term is allowed to further depend on known operational characteristics of the vehicle.

Adaptive Smoothing Spline for Trajectory Reconstruction

2018

Trajectory reconstruction is the process of inferring the path of a moving object between successive observations. In this paper, we propose a smoothing spline-which we name the Vspline-that incorporates position and velocity information and a penalty term that controls acceleration. We introduce a particular adaptive V-spline designed to control the impact of irregularly sampled observations and noisy velocity measurements. A cross-validation scheme for estimating the V-spline parameters is given and we detail the performance of the V-spline on four particularly challenging test datasets. Finally, an application of the V-spline to vehicle trajectory reconstruction in two dimensions is given, in which the penalty term is allowed to further depend on known operational characteristics of the vehicle.

Joint Smoothing and Tracking Based on Continuous-Time Target Trajectory Function Fitting

IEEE Transactions on Automation Science and Engineering, 2019

We present a continuous time state estimation framework that unifies traditionally individual tasks of smoothing, tracking, and forecasting (STF), for a class of targets subject to smooth motion processes, e.g., the target moves with nearly constant acceleration or affected by insignificant noises. Fundamentally different from the conventional Markov transition formulation, the state process is modeled by a continuous trajectory function of time (FoT) and the STF problem is formulated as an online data fitting problem with the goal of finding the trajectory FoT that best fits the observations in a sliding timewindow. Then, the state of the target, whether the past (namely, smoothing), the current (filtering) or the near-future (forecasting), can be inferred from the FoT. Our framework releases stringent statistical modeling of the target motion in real time, and is applicable to a broad range of real world targets of significance such as passenger aircraft and ships which move on scheduled, (segmented) smooth paths but little statistical knowledge is given about their real time movement and even about the sensors. In addition, the proposed STF framework inherits the advantages of data fitting for accommodating arbitrary sensor revisit time, target maneuvering and missed detection. The proposed method is compared with state of the art estimators in scenarios of either maneuvering or non-maneuvering target.

Estimating motion in image sequences

1999

Abstract We have reviewed the estimation of 2D motion from time-varying images, paying particular attention to the underlying models, estimation criteria, and optimization strategies. Several parametric and nonparametric models for the representation of motion vector fields and motion trajectory fields have been discussed. For a given region of support, these models determine the dimensionality of the estimation problem as well as the amount of data that has to be interpreted or transmitted thereafter.

Smoothing for maneuvering target tracking

2006 9th International Conference on Information Fusion, 2006

Smoothing algorithms for maneuvering target tracking with nonlinear target dynamic and measurement equations are described and investigated. Target motion is represented using a multiple model approach. Techniques based on the interacting multiple model filter (IMMF), hypothesis pruning and maximum a posteriori (MAP) estimation of the maneuvering mode are described. All three techniques are based on the use of the unscented transformation with an augmented state model. A procedure for selecting the sigma points which exploits the partial lineairty of the augmented state model is used. The performances of the algorithms are analysed using a scenario involving a target which undergoes coordinated turn maneuvers. In this scenario, for a sufficiently large number of smoothing lags, the MAP approach and the pruning algorithm have almost equal performance and significanty superior performance to the augmented state IMMF. The MAP approach has the benefit of a reduced computational expense.

On the determination of 3D trajectory of moving targets by stereovision

2005

This paper describes a vision system to obtain the 3D trajectory of a moving target by stereovision. For our study, we chose to reconstruct the trajectory of a clay plate that is used in ball-trap. The plate's path can reach 60 meters in length. The trajectory is reconstructed from videos of the moving target and plate positions between the left and right images at different times. To find the 3D coordinates of the plate, we use a triangulation algorithm on couple of points that were found prior. To obtain the whole trajectory of the object, we use a spline interpolation. Our stereovision system is very simple, it uses two classical webcams with a resolution of 640*480 pixels at 30 frames per second. We present in this paper the architecture of the developed system and show its efficiency through experimental results. We conclude with some perspectives for improvement in future work.

Joint Smoothing, Tracking, and Forecasting Based on Continuous-Time Target Trajectory Fitting

—We present a continuous time state estimation framework that unifies traditionally individual tasks of smoothing , tracking, and forecasting (STF), for a class of targets subject to smooth motion processes, e.g., the target moves with nearly constant acceleration or affected by insignificant noises. Fundamentally different from the conventional Markov transition formulation, the state process is modeled by a continuous trajec-tory function of time (FoT) and the STF problem is formulated as an online data fitting problem with the goal of finding the trajectory FoT that best fits the observations in a sliding time-window. Then, the state of the target, whether the past (namely, smoothing), the current (filtering) or the near-future (forecasting), can be inferred from the FoT. Our framework releases stringent statistical modeling of the target motion in real time, and is applicable to a broad range of real world targets of significance such as passenger aircraft and ships which move on scheduled, (segmented) smooth paths but little statistical knowledge is given about their real time movement and even about the sensors. In addition, the proposed STF framework inherits the advantages of data fitting for accommodating arbitrary sensor revisit time, target maneuvering and missed detection. The proposed method is compared with state of the art estimators in scenarios of either maneuvering or non-maneuvering target.

Moving target trajectory estimation using Kalman, curve fitting and Anfis methods

Estimation of the possible position of the moving targets after a few steps has great significance especially in terms of defense industry. If a shoot aiming at the target is planned, the issue of estimation of forward position of the target gains importance in terms of accurate strike of the bullet at the target. In target tracking, impact of three different methods as motion estimation method on various motion types has been examined in our study. Motion types have been examined in four different types, which are rectilinear motion, circular motion, sinusoidal motion and curvilinear motion. On the other hand, estimation methods have been examined under three different titles. These are Kalman estimation method, curve fitting method and Anfis method. Different motion types have been examined with different estimation methods and the results obtained have been presented.

Detection Of Maneuvering Target Tracks

Applications of Digital Image Processing VIII, 1985

This paper presents a new technique for the detection of moving target tracks, where those tracks are linear paths or segments of circles[1.2]. The images used as input represent a time-varying sequence of noisy satellite images of terrain and a moving target(s). Preprocessing of the image sequence involves use of third order differencing to remove stationary points and produce a sequence of intermediate images containing only the target track(s) and noise from various sources[3]. The new procedure described in this paper begins by selection of a window from the preprocessed image sequence. A generalized Hough transform technique is then employed to obtain the equation for the line traveled by any target, and an extension of the linear technique is used to detect circular tracks. New strategies for reduction of the dimensionality of the Hough transformation are also described. The method has been shown to be robust when tested on simulated noisy target tracks.

A New Method for the Computation of Motion from Image Sequences

The object of this paper is to introduce a new method for computing the linear velocity and angular velocity of an unmanned air vehicle (UAV) using only the information obtained from image sequences. In UAV applications, computational resources are limited due to payload constraints and the real-time computation requirement. Therefore, computationally intensive techniques employing feature extraction cannot be used. The alternative, in existing literature, is the computation of optical flow and the subsequent computation of motion. Both of these problems are ill-posed due to the correspondence and aperture problems. In this paper, we consider a different approach for motion estimation that is based on the spatial differentiation of an image function. We show that the solution is a well-posed problem that involves a least squares problem and nonlinear filtering. We also discuss the implementation of such a scheme on a UAV, and discuss the existence of such schemes in insects and crustaceans.