• 전기학회논문지
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• 제56권10호
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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.

• 제어로봇시스템학회논문지
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• 제20권3호
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• pp.313-322
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• 2014

This paper is to survey and put in perspective the working methods of multi-target tracking in clutter. This paper includes theories and practices for data association and related filter structures and is motivated by increasing interest in the area of target tracking, security, surveillance, and multi-sensor data fusion. It is hoped that it will be useful in view of taking into consideration a full understanding of existing techniques before using them in practice.

• 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.

• 제어로봇시스템학회논문지
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• 제7권1호
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• pp.1226-1230
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• 2001

Multi-target tracking systems need to tracking several targets simultaneously. To track a target among the measurements of several targets, data association is needed. In this paper, a method using the cou-pled confidence region of predicted target position is proposed. The proposed method shows good performance in simulations of multi-target tracking systems.

• 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.

• 항공우주기술
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• 제11권2호
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• pp.171-181
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• 2012

항공관제시스템을 구성하는 하부 시스템중 하나인 감시자료처리시스템(SDP, Surveillance Data Processor)은 항공 감시 레이더 등 다양한 감시 센서로부터 감시자료를 전달 받아 항공기의 항적을 추적하는 시스템으로서, SDP의 항적 추적 성능은 항공기의 안전 운항에 직접적인 영향을 미친다. 따라서 개발과정에서 SDP의 요구 성능에 대한 검증은 필수적이며, 특히 대표적인 다중 센서 다중 타겟 추적(Multi-Sensor Multi-Target Tracking)시스템으로서 다양한 타겟 추적 방법이 존재함에 따라 정량적인 추적 정확도 성능 평가가 중요하게 여겨지고 있다. 본 연구에서는 현재 한국항공우주연구원에서 개발 중인 SDP의 항적 추적 성능 검증을 위한 요구 성능 정의, 테스트 환경 구축, 테스트 결과에 대해 정리하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권4호
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• pp.1066-1079
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• 2023

Multi-target tracking based on the detector is a very hot and important research topic in target tracking. It mainly includes two closely related processes, namely target detection and target tracking. Where target detection is responsible for detecting the exact position of the target, while target tracking monitors the temporal and spatial changes of the target. With the improvement of the detector, the tracking performance has reached a new level. The problem that always exists in the research of target tracking is the problem that occurs again after the target is occluded during tracking. Based on this question, this paper proposes a DeepSORT model based on SIFT features to improve ship tracking. Unlike previous feature extraction networks, SIFT algorithm does not require the characteristics of pre-training learning objectives and can be used in ship tracking quickly. At the same time, we improve and test the matching method of our model to find a balance between tracking accuracy and tracking speed. Experiments show that the model can get more ideal results.

• 전자공학회논문지A
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• 제29A권11호
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• pp.70-77
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• 1992

Generally, the conventional tracking algorithms are very limited in the practical applications because of that the computation load is exponentially increased as the number of targets being tracked is increase. Recently, to overcome this kind of limitation, some new tracking methods based on neural network algorithms which have learning and parallel processing capabilities are introduced. By application of neural networks to multi-target tracking problems, the tracking system can be made computationally independent of the number of objects being tracked, through their characteristics of massive parallelism and dense interconnectivity. In this paper, a new neural network tracking algorithm, which has capability of adaptive target tracking with little increase of the amount of calculation under the clutter and noisy environments, is suggested and the possibility of real-time multi-target tracking system based on neural networks is also demonstrated through some good computer simulation results.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권6호
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• pp.1635-1656
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• 2023

In Bayesian multi-target tracking, the Poisson multi-Bernoulli mixture (PMBM) filter is a state-of-the-art filter based on the methodology of random finite set which is a conjugate prior composed of Poisson point process (PPP) and multi-Bernoulli mixture (MBM). In order to improve the random finite set-based filter utilized in multi-target tracking of sensor scanning, this paper introduces the Poisson multi-Bernoulli mixture filter into time-matching Bayesian filtering framework and derive a tractable and principled method, namely: the time-matching Poisson multi-Bernoulli mixture (TM-PMBM) filter. We also provide the Gaussian mixture implementation of the TM-PMBM filter for linear-Gaussian dynamic and measurement models. Subsequently, we compare the performance of the TM-PMBM filter with other RFS filters based on time-matching method with different birth models under directional continuous scanning and out-of-order discontinuous scanning. The results of simulation demonstrate that the proposed filter not only can effectively reduce the influence of sampling time diversity, but also improve the estimated accuracy of target state along with cardinality.

• 한국군사과학기술학회지
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• 제23권3호
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• pp.204-212
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• 2020

For general target tracking works by receiving a set of measurements from sensor. However, if the SNR(Signal to Noise Ratio) is low due to small RCS(Radar Cross Section), caused by remote small targets, the target's information can be lost during signal processing. TBD(Track Before Detect) is an algorithm that performs target tracking without threshold for detection. That is, all sensor data is sent to the tracking system, which prevents the loss of the target's information by thresholding the signal intensity. On the other hand, using all sensor data inevitably leads to computational problems that can severely limit the application. In this paper, we propose an iterative Joint Integrated Probabilistic Data Association as a practical target tracking technique suitable for a low SNR multi-target environment with real time operation capability, and verify its performance through simulation studies.