• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.99-110
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• 2017

Improving tracking performance by estimating the status of multiple targets using radar is important. In a clutter environment, a joint event occurs between the track and measurement in multiple target tracking using a tracking filter. As the number increases, the joint event increases exponentially. The problem to be considered when multiple target tracking filter design in such environments is that first, the tracking filter minimizes the rate of false track alarmsby eliminating the false track and quickly confirming the target track. The purpose is to increase the FTD performance. The second consideration is to improve the track maintenance performance by allocating each measurement to a track efficiently when an event occurs. Through two considerations, a single target tracking data association technique is extended to a multiple target tracking filter, and representative algorithms are JIPDAF and LM-IPDAF. In this study, a probabilistic evaluation of many hypotheses in the assignment of measurements was not performed, so that the computation amount does not increase nonlinearly according to the number of measurements and tracks, and the track existence probability based on the track density The LM-IPDAF algorithm was introduced. This paper also proposes a method to reduce the computational complexity by improving the clutter density estimation method for calculating the track existence probability of LM-IPDAF. The performance was verified by a comparison with the existing algorithm through simulation. As a result, it was possible to reduce the simulation processing time by approximately 20% while achieving equivalent performance on the position RMSE and Confirmed True Track.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.38 no.2
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• pp.129-136
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• 2014

Target-tracking systems are important for carrying out effective surveillance missions using mobile surveillance robots. In this paper, we propose a target-tracking algorithm using camera image data for a three-axis mobile surveillance robot and carry out an actual hardware test for verifying the proposed algorithm. The heading direction vector of a camera system is deduced from the position error between the viewfinder center and the object center in a camera image. The position error is obtained using the CAMShift(Continuously Adaptive Mean Shift) algorithm, an image processing technique. The performance test of an actual three-axis mobile surveillance robot was carried out for verifying the proposed target-tracking algorithm in a real environment.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.559-566
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• 2006

In the real system application, the interacting multiple model (IMM) based algorithm requires less computing resources as well as a good performance with respect to the various target maneuverings. And it further requires an easy design procedure in terms of its structures and parameters. To solve these problems, a fuzzy interacting multiple model (FIMM) algorithm, which is based on the basis sub-models defined by considering the maneuvering property and the time-varying mode transition probabilities designed by using the mode probabilities as inputs of a fuzzy decision maker, is proposed. To verify the performance of the proposed algorithm, airborne target tracking is performed. Simulation results show that the FIMM algorithm solves all problems in the real system application of the IMM based algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.363-363
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• 2000

The conventional algorithms which identify and follow a moving target using a camera located at a fixed position are not appropriate for applying to the cases o( using mobile robots, due to their long processing time. This paper proposes a new tracking algorithm based on the sensing system which uses a line light with a single camera. The algorithm categirizes the motion patterns of a pair of mobile objects into parallel, branching, and merging motion, to decide of which objects the trajectories should be calculated to follow the reference object. Kalman Filter is used to estimate the trajectories of selected objects. The proposed algorithm has shown in the experiments that the mobile robot does not miss the target in most cases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5624-5638
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• 2016

A novel method based on Surf points is proposed to detect and lock-track single ground target in aerial videos. Videos captured by moving cameras contain complex motions, which bring difficulty in moving object detection. Our approach contains three parts: moving target template detection, search area estimation and target tracking. Global motion estimation and compensation are first made by grids-sampling Surf points selecting and matching. And then, the single ground target is detected by joint spatial-temporal information processing. The temporal process is made by calculating difference between compensated reference and current image and the spatial process is implementing morphological operations and adaptive binarization. The second part improves KALMAN filter with surf points scale information to predict target position and search area adaptively. Lastly, the local Surf points of target template are matched in this search region to realize target tracking. The long-term tracking is updated following target scaling, occlusion and large deformation. Experimental results show that the algorithm can correctly detect small moving target in dynamic scenes with complex motions. It is robust to vehicle dithering and target scale changing, rotation, especially partial occlusion or temporal complete occlusion. Comparing with traditional algorithms, our method enables real time operation, processing $520{\times}390$ frames at around 15fps.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.47-54
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• 2017

Recently, correlation filter based trackers have shown excellent tracking performance and computational efficiency. In order to enhance tracking performance in the correlation filter based tracker, search area which is image patch for finding target must include target. In this paper, two methods to discriminatively represent target in the search area are proposed. Firstly, search area location is estimated using pyramidal Lucas-Kanade algorithm. By estimating search area location before filtering, fast motion target can be included in the search area. Secondly, we investigate multi-channel Local Edge Pattern(LEP) which is insensitive to illumination and noise variation. Qualitative and quantitative experiments are performed with eight dataset, which includes ground truth. In comparison with method without search area estimation, our approach retain tracking for the fast motion target. Additionally, the proposed multi-channel LEP improves discriminative performance compare to existing features.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.7
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• pp.587-594
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• 2005

In this paper, a robust correlation-based video tracking is proposed to track a moving object in correlated image sequences. A correlation-based video tracking algorithm seeks to align the incoming target image with the reference target block image, but has critical problems, so called a false-peak problem and a drift phenomenon (correlator walk-off. The false-peak problem is generally caused by highly correlated background pixels with similar intensity of a moving target and the drift phenomenon occurs when tracking errors accumulate from frame to frame because of the nature of the correlation process. At first, the false-peaks problem for the ordinary correlation-based video tracking is investigated using a simple mathematical analysis. And, we will suggest a robust selective-attention correlation measure with a gradient preprocessor combined by a drift removal compensator to overcome the walk-off problem. The drift compensator adaptively controls the template block size according to the target size of interest. The robustness of the proposed method for practical application is demonstrated by simulating two real-image sequences.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.1
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• pp.16-21
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• 2003

The accuracy in maneuvering target tracking using multiple models is resulted in by the suitability of each target motion model to be used. The interacting multiple model (IMM) method and the adaptive IMM (AIMM) method require the predefined sub-models and the predetermined acceleration intervals, respectively, in consideration of the properties of maneuvers in order to construct multiple models. In this paper, to solve these problems, a genetic algorithm(GA) based-IMM method using fuzzy logic is proposed. In the proposed method, the acceleration input is regarded as an additive noise and a sub-model is represented as a set of fuzzy rules to calculate the time-varying variances of the process noises of a new piecewise constant white acceleration model. The proposed method is compared with the AIMM algorithm in simulation.

• Journal of Electrical Engineering and Technology
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• v.12 no.5
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• pp.2067-2078
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• 2017

In this paper, a new face tracking algorithm is proposed. The CamShift (Continuously adaptive mean SHIFT) algorithm shows unstable tracking when there exist objects with similar color to that of face in the background. This drawback of the CamShift is resolved by the proposed algorithm using Kinect's pixel-by-pixel depth information and the skin detection method to extract candidate skin regions in HSV color space. Additionally, even when the target face is disappeared, or occluded, the proposed algorithm makes it robust to this occlusion by the feature point matching. Through experimental results, it is shown that the proposed algorithm is superior in tracking performance to that of existing TLD (Tracking-Learning-Detection) algorithm, and offers faster processing speed. Also, it overcomes all the existing shortfalls of CamShift with almost comparable processing time.

• Journal of the Korean Institute of Telematics and Electronics A
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• v.29A no.2
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• pp.47-54
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• 1992

In this paper, a new extended JPDA (Joint Probabilistic Data Association) tracking algorithm which has more excellent performance than that of the conventional JPDA algorithm in case of the tracking of crossing targets is proposed. In the proposed extended JPDA algorithm, the velocity parameters as well as the position parameters are included to compute the association probabilities between tracks and measurement data. Then the tracking performance of crossing targets is improved and the track bias of parallel moving targets can be reduced. Accordingly, in this paper, the new extended JPDA algorithm for multitarget tracking is proposed and its good performance is shown through the computer simulation. And, tracking performance of extended JPDA algorithm is also compared with that of JPDA algorithm with our noise model.