• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.10
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• pp.1826-1835
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• 2007

In this paper, we present a new approach for automatic detection and tracking for multiple targets. We combine a highest probability data association(HPDA) algorithm for target detection with a particle filter for multiple target tracking. The proposed approach evaluates the probabilities of one-to-one assignments of measurement-to-track and the measurement with the highest probability is selected to be target- originated, and the measurement is used for probabilistic weight update of particle filtering. The performance of the proposed algorithm for target tracking in clutter is compared with the existing clustering algorithm and the sequential monte carlo method for probability hypothesis density(SMC PHD) algorithm for multi-target detection and tracking. Computer simulation studies demonstrate that the HPDA algorithm is robust in performing automatic detection and tracking for multiple targets even though the environment is hostile in terms of high clutter density and low target detection probability.

• Journal of Information Processing Systems
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• v.15 no.2
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• pp.344-358
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• 2019

Surveillance cameras have installed in many places because security and safety is becoming important in modern society. Through surveillance cameras installed, we can deal with troubles and prevent accidents. However, watching surveillance videos and judging the accidental situations is very labor-intensive. So now, the need for research to analyze surveillance videos is growing. This study proposes an algorithm to track multiple persons using SURF and background subtraction. While the SURF algorithm, as a person-tracking algorithm, is robust to scaling, rotating and different viewpoints, SURF makes tracking errors with sudden changes in videos. To resolve such tracking errors, we combined SURF with a background subtraction algorithm and showed that the proposed approach increased the tracking accuracy. In addition, the background subtraction algorithm can detect persons in videos, and SURF can initialize tracking targets with these detected persons, and thus the proposed algorithm can automatically detect the enter/exit of persons.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.417-420
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• 1998

A heuristic sweeping algorithm for an autonomous smearing robot which executes the area filling task is proposed. This algorithm searches tracking points with the obstacle andenvironment wall while the robot tracking whole workspace, and finds sequential tracking line by sequentally connecting the tracking points in such a way that (1) the line should be never crossed, (2) the total tracking points should be is linked as short as possible, and (3) the tracking link should be cross over the obstacle in the work-space. If the line pass through the obstacle, hierarchical collision free algorithm proposed is implied. The proposed algorithm consists of (1) collision detection procedure, (2) obstacle map making procedures, (3) tracking points generation procedures for subgosls, (4) tracking points scanning procedures, and (5) obstacle avoidance procedure.

• ETRI Journal
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• v.45 no.3
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• pp.394-403
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• 2023

Direct tracking problem of moving noncircular sources for multiple arrays is investigated in this study. Here, we propose an improved unscented particle filter (I-UPF) direct tracking method, which combines system proportional symmetry unscented particle filter and Markov Chain Monte Carlo (MCMC) algorithm. Noncircular sources can extend the dimension of sources matrix, and the direct tracking accuracy is improved. This method uses multiple arrays to receive sources. Firstly, set up a direct tracking model through consecutive time and Doppler information. Subsequently, based on the improved unscented particle filter algorithm, the proposed tracking model is to improve the direct tracking accuracy and reduce computational complexity. Simulation results show that the proposed improved unscented particle filter algorithm for noncircular sources has enhanced tracking accuracy than Markov Chain Monte Carlo unscented particle filter algorithm, Markov Chain Monte Carlo extended Kalman particle filter, and two-step tracking method.

• Journal of Advanced Navigation Technology
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• v.23 no.1
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• pp.77-83
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• 2019

In this paper, we propose an object tracking algorithm based on color histogram and convolutional neural network model. In order to increase the tracking accuracy, we synthesize generic object tracking using regression network algorithm which is one of the convolutional neural network model-based tracking algorithms and a mean-shift tracking algorithm which is a color histogram-based algorithm. Both algorithms are classified through support vector machine and designed to select an algorithm with higher tracking accuracy. The mean-shift tracking algorithm tends to move the bounding box to a large range when the object tracking fails, thus we improve the accuracy by limiting the movement distance of the bounding box. Also, we improve the performance by initializing the tracking start positions of the two algorithms based on the average brightness and the histogram similarity. As a result, the overall accuracy of the proposed algorithm is 1.6% better than the existing generic object tracking using regression network algorithm.

• 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.

• Journal of the Semiconductor & Display Technology
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• v.18 no.1
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• pp.53-58
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• 2019

Since floating facility with mooring system can be moved and rotated by wind or other environmental variables, the error in azimuthal angle must be compensated using a GPS receiver and geo-magnetic sensor. Accordingly, when an existing photovoltaic tracking algorithm is applied to a floating photovoltaic system, it is difficult to do the optimal solar tracking. In this paper, an effective azimuthal angle algorithm is develop for the photovoltaic tracking in floating condition. In order to verify the developed algorithm, the prototype of the floating photovoltaic system is manufactured and the developed algorithm is applied to the system. The algorithm shows a good tracking feasibility on the prototype.

• The Journal of the Acoustical Society of Korea
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• v.22 no.1E
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• pp.19-25
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• 2003

Ryu et al. recently proposed a multiple target angle-tracking algorithm without a data association problem. This algorithm, however, shows the degraded performance on evasive maneuvering targets, because the estimated signal subspace is d,:graded in the algorithm. In this Paper, we proposed a new algorithm, in which VFF-PASTd (Variable Forgetting Factor PASTd) algorithm is applied to Ryu's algorithm to effectively handle the evasive target tracking with better time-varying signal subspace.

• Journal of Broadcast Engineering
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• v.21 no.6
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• pp.861-877
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• 2016

This paper first discusses the disadvantages of the existing CamShift Algorithm for real time face tracking, and then proposes a new Camshift Algorithm that performs better than the existing algorithm. The existing CamShift Algorithm shows unstable tracking when tracing similar colors in the background of objects. This drawback of the existing CamShift is resolved by using Kinect’s pixel-by-pixel depth information and the Skin Detection algorithm to extract candidate skin regions based on HSV color space. Additionally, even when the tracking object is not found, or when occlusion occurs, the feature point-based matching algorithm makes it robust to occlusion. By applying the improved CamShift algorithm to face tracking, the proposed real-time face tracking algorithm can be applied to various fields. The results from the experiment prove that the proposed algorithm is superior in tracking performance to that of existing TLD tracking algorithm, and offers faster processing speed. Also, while the proposed algorithm has a slower processing speed than CamShift, it overcomes all the existing shortfalls of the existing CamShift.