• Journal of Broadcast Engineering
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• v.8 no.2
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• pp.172-180
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• 2003

In this paper, we propose fast and precise extracting & tracking algorithm based on general camera and frame grabber for facial motion capture animation. Proposed algorithm consists of two steps. extracting and tracking. The former is to separate multiple markers from input image using region merging based on neural networks. The latter Is to track extracted multiple markers at each frame using tracking algorithm based on neural networks. In the experiment, we could remove noise and reduce processing time in the step of extraction. In addition, we could have good tracking results in the low frame rates.

• Journal of IKEEE
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• v.6 no.1 s.10
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• pp.54-64
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• 2002

In this paper, we propose the algorithm for segmenting the moving speaker and tracking its movement in the video conference image. For real time processing, we simplify the algorithm which is processed in the order of the segmenting and the tracking step. In the segmenting step, the speaker object is segmented from the image by using both the motion information obtained from the difference method and the illuminance information of image. The reference mask image is created from segmented speaker object. In the tracking step, the moving speaker is tracked by using simple block matching algorithm of which computation time is reduced by discarding the blocks which are classified into the unuseful blocks. In the simulation, we can get the good result of segmenting and tracking the moving speaker by applying the proposed algorithm to several test images.

• Journal of Advanced Navigation Technology
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• v.10 no.4
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• pp.400-405
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• 2006

In this paper, we propose a moving object tracking algorithm by using motion estimation in UAV(Unmanned Aerial Vehicle) video. Proposed algorithm is based on generation of initial image from detected reference image, and tracking of moving object under the time-varying image. With a series of this procedure, tracking process is stable even when the UAV camera sways by correcting position of moving object, and tracking time is relatively reduced. A block matching algorithm is also utilized to determine the similarity between reference image and moving object. An experimental result shows that our proposed algorithm is better than the existing full search algorithm.

• Journal of Drive and Control
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• v.16 no.2
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• pp.22-29
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• 2019

This study presented a model predictive tracking control algorithm of autonomous truck based on object state estimation using extended Kalman filter. To design the model, the 1-layer laser scanner was used to estimate position and velocity of the object using extended Kalman filter. Based on these estimations, the desired linear path for object tracking was computed. The lateral and yaw angle errors were computed using the computed linear path and relative positions of the truck. The computed errors were used in the model predictive control algorithm to compute the optimal steering angle for object tracking. The performance evaluation was conducted on Matlab/Simulink environments using planar truck model and actual point data obtained from laser scanner. The evaluation results showed that the tracking control algorithm developed in this study can track the object reasonably based on the model predictive control algorithm based on the estimated states.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.9
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• pp.2642-2664
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• 2024

In the process of modern traffic management, information technology has become an important part of intelligent traffic governance. Real-time monitoring can accurately and effectively track and record vehicles, which is of great significance to modern urban traffic management. Existing tracking algorithms are affected by the environment, viewpoint, etc., and often have problems such as false detection, imprecise anchor boxes, and ID switch. Based on the YOLOv5 algorithm, we improve the loss function, propose a new feature extraction module to obtain the receptive field at different scales, and do adaptive fusion with the SGE attention mechanism, so that it can effectively suppress the noise information during feature extraction. The trained model improves the mAP value by 5.7% on the public dataset UA-DETRAC without increasing the amount of calculations. Meanwhile, for vehicle feature recognition, we adaptively adjust the network structure of the DeepSort tracking algorithm. Finally, we tested the tracking algorithm on the public dataset and in a realistic scenario. The results show that the improved algorithm has an increase in the values of MOTA and MT etc., which generally improves the reliability of vehicle tracking.

• 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.

• Journal of Electrical Engineering and information Science
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• v.3 no.2
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• pp.267-273
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• 1998

This paper introduces an adaptive algorithm determining the measurement-track association problem in multi-target tracking(MTT). We model the target and measurement relationships with mean field theory and then define a MAP estimate for the optimal association. Based on this model, we introduce an energy function defined over the measurement space, that incorporates the natural constraints for target tracking. To find the minimizer of the energy function, we derived a new adaptive algorithm by introducing the Lagrange multipliers and local dual theory. Through the experiments, we show that this algorithm is stable and works well in general environments. Also the advantages of the new algorithm over other algorithms are discussed.

• 제어로봇시스템학회:학술대회논문집
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• 1998.10a
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• pp.323-328
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• 1998

In this paper, a new input estimation algorithm is proposed for target tracking problem. The unknown target maneuver is approximated by a linear combination of independent time functions and the coefficients are estimated by using a weighted least-squares estimation technique. The proposed algorithm is verified by computer simulation of a realistic two-dimensional tracking problem. The proposed algorithm provides significant improvements in estimation performance over the conventional input estimation techniques based on the constant-input assumption.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.405-406
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• 2007

In this paper, we propose feature extraction and tracking algorithm using multi resolution in 2-dimensional wavelet domain. Feature extraction selects feature points using 2-level wavelet transform in interested region. Feature tracking estimates displacement between current frame and next frame based on feature point which is selected feature extraction algorithm. Experimental results show that the proposed algorithm confirmed a better performance than the existing other algorithms.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.943-945
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• 1996

In this paper, we present an algorithm that allows tracking of a target using measurements obtained from a sensor with limited resolution. The Interacting Multiple Model (IMM) algorithm has been shown to be one of the most cost-effective estimation schemes for hybrid systems. The approach consists of IMM algorithm combined with a coupled version of the Joint Probabilistic Data Association Filter for the target that splits into two targets.