• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1287-1300
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• 2018

Target-tracking algorithm based on ad hoc camera sensor networks (ACSNs) utilizes the distributed observation capability of nodes to achieve accurate target tracking. A compressed sensing-based multiple-target tracking algorithm (CSMTTA) for ACSNs is proposed in this work based on the study of camera node observation projection model and compressed sensing model. The proposed algorithm includes reconfiguration of observed signals and evaluation of target locations. It reconfigures observed signals by solving the convex optimization of L1-norm least and forecasts node group to evaluate a target location by the motion features of the target. Simulation results show that CSMTTA can recover the subtracted observation information accurately under the condition of sparse sampling to a high target-tracking accuracy and accomplish the distributed tracking task of multiple mobile targets.

• ETRI Journal
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• v.41 no.4
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• pp.483-493
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• 2019

Considering the high dimensions of video sequences, it is often challenging to acquire a sufficient dataset to train the tracking models. From this perspective, we propose to revisit the idea of hand-crafted feature learning to avoid such a requirement from a dataset. The proposed tracking approach is composed of two phases, detection and tracking, according to how severely the appearance of a target changes. The detection phase addresses severe and rapid variations by learning a new appearance model that classifies the pixels into foreground (or target) and background. We further combine the raw pixel features of the color intensity and spatial location with convolutional feature activations for robust target representation. The tracking phase tracks a target by searching for frame regions where the best pixel-level agreement to the model learned from the detection phase is achieved. Our two-phase approach results in efficient and accurate tracking, outperforming recent methods in various challenging cases of target appearance changes.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.115-117
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• 2014

Recently, interesting of U-city with ubiquitous computing technologies has increased and u-city services can improve people's quality of life. Among the u-city services, traffice service is actively developed in our lives. In this paper, we propose the business model and business model process in u-car tracking service. To propose the research purpose, we examined the prior business model and investigated u-car tracking information. And also, we draw scenario and used it to identify business model. To efficiently understand proposed business model, we built business model process of u-car tracking service. The results of study, we proposed business model and business model process with stakeholder, benefiter and value model. This study concludes with implications of the study results as well as limitations and future research directions.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.93-100
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• 2003

Accuracy in maneuvering target tracking using multiple models relies upon the suit-ability of each target motion model to be used. To construct multiple models, the interacting multiple model (IMM) algorithm and the adaptive IMM (AIMM) algorithm require predefined sub-models and predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers. To solve these problems, this paper proposes the GA-based IMM method as an intelligent tracking method for a maneuvering target. In the proposed method, the acceleration input is regarded as an additive process noise, a sub-model is represented as a fuzzy system to compute the time-varying variance of the overall process noise, and, to optimize the employed fuzzy system, the genetic algorithm (GA) is utilized. The simulation results show that the proposed method has a better tracking performance than the AIMM algorithm.

• Proceedings of the KIEE Conference
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• 2001.11c
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• pp.438-441
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• 2001

This paper describes a system for tracking a face in a input video sequence using facial convex hull based facial segmentation and a robust hausdorff distance. The algorithm adapts YCbCr color model for classifying face region by [l]. Then, we obtain an initial face model with preprocessing and convex hull. For tracking, a Robust Hausdorff distance is computed and the best possible displacement is selected. Finally, the previous face model is updated using the displacement t. It is robust to some noises and outliers. We provide an example to illustrate the proposed tracking algorithm in video sequences obtained from CCD camera.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.126-133
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• 2017

The conventional hand-crafted features used to track objects have limitations in object representation. Convolutional neural networks, which show good performance results in various areas of computer vision, are emerging as new ways to break through the limitations of feature extraction. CNN extracts the features of the image through layers of multiple layers, and learns the kernel used for feature extraction by itself. In this paper, we use the feature map extracted from the convolution layer of the convolution neural network to create an outline model of the object and use it for tracking. We propose a method to adaptively update the outline model to cope with various environment change factors affecting the tracking performance. The proposed algorithm evaluated the validity test based on the 11 environmental change attributes of the CVPR2013 tracking benchmark and showed excellent results in six attributes.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.391-395
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• 1996

This paper presents a robust force tracking control of a small-sized SMA gripper with two fingers using shape memory alloy(SMA) actuators. The mathematical governing equation of the proposed system is derived by Hamilton's principle and Lagrangian equation and then, the control system model is integrated with the first-order actuator dynamics. Uncertain system parameters such as time constant of the actuators are also included in the control model. A robust two degree of freedom(TDF) controller using H$_{\infty}$ control theory, which has inherent robustness to model uncertainties and external disturbances, is adopted to achieve end-point force tracking control of the two-finger gripper. Force tracking control performances for desired trajectories represented by sinusoidal and step functions are evaluated by undertaking both simulation and experimental works.

• Journal of Internet Computing and Services
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• v.9 no.3
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• pp.91-101
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• 2008

This paper proposes component-based density propagation for tracking a component-based human body model that comprises components and their flexible links. We divide a human body into six body parts as components - head, body, left arm, right arm, left foot, and right foot - that are most necessary in tracking its movement. Instead of tracking a whole body's silhouette, using component-based density propagation, the proposed method individually tracks each component of various parts of human body through a human body model connecting the components. The proposed human body tracking system has been applied to track movements usee for young children's movement education: balancing, hopping, jumping, walking, turning, bending, and stretching. This proposed system demonstrated the validity and effectiveness of movement tracking by independently detecting each component in the human body model and by acquiring an average 97% of high tracking rate.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.5
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• pp.445-453
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• 2001

The multiple targets tracking problem has been one of the main issues in the radar applications area in the last decade. Besides the standard Kalman filtering, various methods including the variable dimen-sion filter, input estimation filter, interacting multiple model(IMM) filter, dederated variable dimension filter with input estimation, etc., have proposed to address the tracking and sensor fusion issues. In this pa- per, two existing tracking algorithm, i.e, the IMM filter and the variable dimension filter with input estima-tion(VDIE), are combined for the purpose of improving the tracking performance for maneuvering targets. To evaluate the tracking performance of the proposed algorithm, three typical maneuvering patterns, i.e., waver, pop-up, and high-diver motions, are defined and are applied to the modified IMM filter as well as the standard IMM filter. The smaller RMS tracking errors, in position and velocity, of the modified IMM filter than the standard IMM filter are demonstrated though computer simulations.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.3
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• pp.405-413
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• 2011

Defense Systems Test Center in ADD supports increasingly various missile test requirements such as higher altitude event, multi target operation and low-altitude, high velocity target tracking. In this paper, we have proposed the development of instrumentation radar tracking status simulator based on virtual reality. This simulator can predict the tracking status and risk of failure using several modeling algorithms. It consists of target model, radar model, environment model and several algorithms includes the multipath interference effects. Simulation results show that the predict tracking status and signal are similar to the test results of the live flight test. This simulator predicts and analyze all of the status and critical parameters such as the optimal site location, servo response, optimal flight trajectory, LOS(Line of Sight). This simulator provides the mission plan with a powerful M&S tool to rehearse and analyze instrumentation tracking radar measurement plan for live flight test at DSTC(Defense Systems Test Center).