• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권10호
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• pp.5112-5128
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• 2016

Tracking human body is an important problem in computer vision field. Tracking failures caused by occlusion can lead to wrong rectification of the target position. In this paper, a robust human tracking algorithm is proposed to address the problem of occlusion, rotation and improve the tracking accuracy. It is based on Tracking-Learning-Detection framework. The key auxiliary information is used in the framework which motivated by the fact that a tracking target is usually embedded in the context that provides useful information. First, face localization method is utilized to find key face location information. Second, the relative position relationship is established between the auxiliary information and the target location. With the relevant model, the key face information will get the current target position when a target has disappeared. Thus, the target can be stably tracked even when it is partially or fully occluded. Experiments are conducted in various challenging videos. In conjunction with online update, the results demonstrate that the proposed method outperforms the traditional TLD algorithm, and it has a relatively better tracking performance than other state-of-the-art methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권10호
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• pp.5095-5111
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• 2016

The Probability Hypothesis Density (PHD) filter is a suboptimal approximation and tractable alternative to the multi-target Bayesian filter based on random finite sets. However, the PHD filter fails to track newborn targets when the target birth intensity is unknown prior to tracking. In this paper, a dual detection-guided newborn target intensity PHD algorithm is developed to solve the problem, where two schemes, namely, a newborn target intensity estimation scheme and improved measurement-driven scheme, are proposed. First, the newborn target intensity estimation scheme, consisting of the Dirichlet distribution with the negative exponent parameter and target velocity feature, is used to recursively estimate the target birth intensity. Then, an improved measurement-driven scheme is introduced to reduce the errors of the estimated number of targets and computational load. Simulation results demonstrate that the proposed algorithm can achieve good performance in terms of target states, target number and computational load when the newborn target intensity is not predefined in multi-target tracking systems.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.108-113
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• 1991

In this paper, an improved method for tracking maneuvering target is proposed. The proposed tracking filter is constructed by combining the input estimation approach with the variable dimension filtering approach. In this approach, the filter also provides the estimated time instant at which target starts maneuver, when the target maneuver is detected. Using this estimated maneuvering time, the maneuver input is estimated and the tracking system changes to the maneuver model. Simulations are performed to demonstrate the efficiency of the proposed tracking filter.

• 제어로봇시스템학회논문지
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• 제20권1호
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• pp.29-36
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• 2014

In this paper, a novel MBE (Model Based target size Estimator) is presented for SDIIR (Strap Down Imaging Infrared) seekers. The target tracking requires the target size information for which residual range between target and missile should be provided. Unfortunately, in general, the missile with passive sensor such as IIR (Imaging Infrared), CCD (Coupled Charging Device) cannot obtain range information. To overcome the problem, the proposed method enables the SDIIR seeker to estimates target size by using target size model and track the target. The performance of proposed method is tested at IIR target tracking of target intercept scenario. The experiment results show that the proposed algorithm has the relatively good performance.

• 한국음향학회지
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• 제33권4호
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• pp.225-231
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• 2014

본 논문은 해양에서 발생하는 음향 표적에 대해서 표적의 식별 정보 피드백을 통해 관심 표적 추적 성능을 향상시키는 기법을 제안한다. 기존의 다중주파수 표적 추적 기법은 탐지된 음향신호 특성만 활용하여 추적을 수행하는 반면, 본 논문에서 제안하는 기법은 탐지된 음향신호 특성과 더불어 관심 표적의 특징인자가 식별되는지 여부를 추적에 활용한다. 제안하는 기법은 특징인자가 식별되는 측정치에 대해서만 추적을 수행하여 관심 표적이 아닌 표적에 대한 불필요한 추적을 방지한다. 또한 특징인자가 식별된 표적을 추적할 때 동적으로 추적 파라메터를 최적화하여 관심 표적에 대해 지속적이고 안정적인 추적을 수행한다. 모의 신호를 이용하여 실험을 수행하였으며 미약한 신호가 나타나는 수중 표적에 대해 제안하는 기법을 적용한 결과, 불필요한 표적 개수가 감소하고 관심 표적의 추적이 지속적으로 안정되게 수행됨을 확인하였다.

• 제어로봇시스템학회논문지
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• 제6권2호
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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.

• 한국군사과학기술학회지
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• 제11권1호
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• pp.75-84
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• 2008

A new automatic small target detection and tracking algorithm for the real-time IR surveillance system is presented. The automatic target detection and tracking algorithm of the real-time systems, requires low complexity and robust tracking performance in the cluttered environment. Linear-array and parallel-scan IR systems usually suffer from severe scan noise caused by the detector non-uniformity. After the spatial filtering and thresholding, this scan noise still remains as high amplitude clutter which degrades the target detection rate and tracking performance. In this paper, we propose a new feature which consists of area and validity information of a measurement. By adopting this feature to the measurements selection and track confirmation, we can increase the target detection rate and reduce both the track loss rate and false track rate. From the experimental results, we can validate the feasibility of the proposed method in the noisy IR images.

• 대한임베디드공학회논문지
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• 제9권1호
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• pp.43-52
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• 2014

This paper presents a tracking system using images captured from a camera on a moving platform. A camera on an unmanned flying vehicle generally moves and shakes due to external factors such as wind and the ego-motion of the machine itself. This makes it difficult to track a target properly, and sometimes the target cannot be kept in view of the camera. To deal with this problem, we propose a new system for stable tracking of a target under such conditions. The tracking system includes target tracking and 3-axis camera motion compensation. At the same time, we consider the simulation of the motion of flying vehicles for efficient and safe testing. With 3-axis motion compensation, our experimental results show that robustness and stability are improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권5호
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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.

• ETRI Journal
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• 제41권4호
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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.