• Journal of Institute of Control, Robotics and Systems
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• v.6 no.2
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• pp.173-180
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• 2000

During the last decade many researches have been working on multiple target tracking problem in the area of radar application, Various approaches have been proposed to solve the tracking problem and the concept of sensor fusion was established as an effort. In this paper utilization of geographic information for ground target tracking is investigated and performance comparison with the results of applying sensor fusion is described. Geographic information is used in three aspects: association masking target measurement and re-striction of removing true target. Simulation results indicate that using two sensors shows better performance with respect to tracking but a single with geographic information is a winner in reducing the number of false tracks.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.5
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• pp.1711-1725
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• 2014

Multi-target tracking is the main purpose of many video surveillance applications. Recently, multi-target tracking based on the particle filter method has achieved robust results by using the data association process. However, this method requires many calculations and it is inadequate for real time applications, because the number of associations exponentially increases with the number of measurements and targets. In this paper, to reduce the computational cost of the data association process, we propose a novel multi-target tracking method that excludes particle samples in the overlapped predictive region between the target to track and marginal targets. Moreover, to resolve the occlusion problem, we define an occlusion mode with the normal dynamic mode. When the targets are occluded, the mode is switched to the occlusion mode and the samples are propagated by Gaussian noise without the sampling process of the particle filter. Experimental results demonstrate the robustness of the proposed multi-target tracking method even in occlusion.

• Transactions on Electrical and Electronic Materials
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• v.14 no.4
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• pp.187-192
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• 2013

Because of the importance of small target detection in infrared images, many studies have been carried out in this area. Using a Kalman filter and mean shift algorithm, this study proposes an algorithm to track multiple small moving targets even in cases of target disappearance and appearance in serial infrared images in an environment with many noises. Difference images, which highlight the background images estimated with a background estimation filter from the original images, have a relatively very bright value, which becomes a candidate target area. Multiple target tracking consists of a Kalman filter section (target position prediction) and candidate target classification section (target selection). The system removes error detection from the detection results of candidate targets in still images and associates targets in serial images. The final target detection locations were revised with the mean shift algorithm to have comparatively low tracking location errors and allow for continuous tracking with standard model updating. In the experiment with actual marine infrared serial images, the proposed system was compared with the Kalman filter method and mean shift algorithm. As a result, the proposed system recorded the lowest tracking location errors and ensured stable tracking with no tracking location diffusion.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.478-481
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• 1996

Tracking a target of versatile maneuver recently demands a stable adaptation of tracker, and the multiple model techniques are being developed because of its ability to produce useful information of target maneuver. This paper presents the way to apply the multiple model method in a moving-target and moving-platform scenario, and the estimation and prediction results better than those of single Kalman filter.

• The Journal of the Acoustical Society of Korea
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• v.24 no.1E
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• pp.34-37
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• 2005

Ryu et al. proposed a multiple target angle-tracking algorithm without a data association problem using angular innovations. This algorithm, however, needs the computational loads in proportion to the square number of sensors regardless of the number of targets, because it uses a nonlinear equation between the signal subspace and angular innovation. In this Paper, we proposed an efficient algorithm for the multiple target angle-tracking using angular innovations. The proposed algorithm extracts the angular innovations from noise subspace. Also, it is demonstrated by computer simulations dealing with the tracking of crossing targets. The simulation results show that the computational loads of the proposed algorithm are $80\%$ and $60\%$ of those of Ryu's algorithm for 3 targets and 6 targets without degrading the performance of the target tracking.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.314-324
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• 2018

This paper proposes a linear distributed target tracking filter for multiple unmanned aerial vehicles(UAVs) sharing their passive sensor measurements through communication channels. Different from the conventional nonlinear filtering schemes, the distributed passive target tracking problem is newly formulated within the framework of a linear robust state estimation theory incorporated with a linear uncertain measurement equation including the coordinate transform uncertainty. To effectively cope with the performance degradation due to the coordinate transform uncertainty, a linear consistent robust Kalman filter(CRKF) theory is devised and applied for designing a distributed passive target tracking filter. Through the simulations for typical UAV surveillance mission, the superior performance of the proposed method over the existing schemes of distributed passive target tracking are demonstrated.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.693-698
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• 2016

A decentralized control method is proposed to enable a group of robots to achieve maximum performance in multisensory target tracking while avoiding collision with the target. The decentralized control was designed based on navigation function formalism. The study showed that the multiple agent system converged to the positions providing the maximum performance by the decentralized controller, based on Lyapunov and Hessian theory. An exemplary simulation was given for a multiple agent system tracking a stationary target.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.218-223
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• 1993

In this paper, we modify a multiple target angle tracking algorithm presented by Sword et al.. The predicted estimates, instead of the existing estimates, of the target angles are updated by the most recent output of the sensor array to improve the tracking performance of the algorithm for crossing targets. Also, the least square solution is modified to avoid abnormally large angle innovations when the target angles are very close. The improved performance of the proposed algorithm is demonstrated by computer simulations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.908-928
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• 2022

Multiple target tracking mainly focuses on tracking unknown number of targets in the complex environment of clutter and missed detection. The generalized labeled multi-Bernoulli (GLMB) filter has been shown to be an effective approach and attracted extensive attention. However, in the scenarios where the clutter rate is high or measurement-outliers often occur, the performance of the GLMB filter will significantly decline due to the Gaussian-based likelihood function is sensitive to clutter. To solve this problem, this paper presents a robust GLMB filter and smoother to improve the tracking performance in the scenarios with high clutter rate, low detection probability, and measurement-outliers. Firstly, a Student-T distribution variational Bayesian (TDVB) filtering technology is employed to update targets' states. Then, The likelihood weight in the tracking process is deduced again. Finally, a trajectory smoothing method is proposed to improve the integrative tracking performance. The proposed method are compared with recent multiple target tracking filters, and the simulation results show that the proposed method can effectively improve tracking accuracy in the scenarios with high clutter rate, low detection rate and measurement-outliers. Code is published on GitHub.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.5
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• pp.697-704
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• 2003

In this paper, the visual tracking system for arbitrary shaped moving object is proposed. The established tracking system can be divided into model based method that needs previous model for target object and image based method that uses image feature. In the model based method, the reliable tracking is possible, but simplification of the shape is necessary and the application is restricted to definite target mod el. On the other hand, in the image based method, the process speed can be increased, but the shape information is lost and the tracking system is sensitive to image noise. The proposed tracking system is composed of the extraction process that recognizes the existence of moving object and tracking process that extracts dynamic characteristics and shape information of the target objects. Specially, active contour model is used to effectively track the object that is undergoing shape change. In initializatio n process of the contour model, the semi-automatic operation can be avoided and the convergence speed of the contour can be increased by the proposed effective initialization method. Also, for the efficient solution of the correspondence problem in multiple objects tracking, the variation function that uses the variation of position structure in image frame and snake energy level is proposed. In order to verify the validity and effectiveness of the proposed tracking system, real time tracking experiment for multiple moving objects is implemented.