• Journal of Korea Multimedia Society
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• v.22 no.2
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• pp.167-177
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• 2019

Even though so much progresses have been achieved in Multiple Object Tracking (MOT), most of reported MOT methods are not still satisfactory for commercial embedded products like Pan-Tilt-Zoom (PTZ) camera. In this paper, we propose a real-time multiple pedestrians tracking method for embedded environments. First, we design a new light weight convolutional neural network(CNN)-based pedestrian detector, which is constructed to detect even small size pedestrians, as well. For further saving of processing time, the designed detector is applied for every other frame, and Kalman filter is employed to predict pedestrians' positions in frames where the designed CNN-based detector is not applied. The pose orientation information is incorporated to enhance object association for tracking pedestrians without further computational cost. Through experiments on Nvidia's embedded computing board, Jetson TX2, it is verified that the designed pedestrian detector detects even small size pedestrians fast and well, compared to many state-of-the-art detectors, and that the proposed tracking method can track pedestrians in real-time and show accuracy performance comparably to performances of many state-of-the-art tracking methods, which do not target for operation in embedded systems.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.3
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• pp.211-217
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• 2011

This paper proposes a quasi-optimal linear DOA (Direction-of-Arrival) estimator which is necessary for the development of a real-time robot auditory system tracking moving acoustic source. It is well known that the use of conventional nonlinear filtering schemes may result in the severe performance degradation of DOA estimation and not be preferable for real-time implementation. These are mainly due to the inherent nonlinearity of the acoustic signal model used for DOA estimation. This motivates us to consider a new uncertain linear acoustic signal model based on the linear prediction relation of a noisy sinusoid. Using the suggested measurement model, it is shown that the resultant DOA estimation problem is cast into the NCRKF (Non-Conservative Robust Kalman Filtering) problem [12]. NCRKF-based DOA estimator provides reliable DOA estimates of a fast moving acoustic source in spite of using the noise-corrupted measurement matrix in the filter recursion and, as well, it is suitable for real-time implementation because of its linear recursive filter structure. The computational efficiency and DOA estimation performance of the proposed method are evaluated through the computer simulations.

• International Journal of Computer Science & Network Security
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• v.21 no.5
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• pp.31-34
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• 2021

The article considers the possibilities of increasing the accuracy of estimates of the parameters of the trajectory of the target with the provision of a given probability of stable support of the air object, in particular, during its maneuver. The aim of the work is to develop a filtration algorithm that provides a given probability of stable tracking of the air object by determining the regular components of filtration errors, in particular, when maneuvering the air object, and their compensation with appropriate correction of filter parameters and estimates of air object trajectory parameters.

• Journal of the Society of Naval Architects of Korea
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• v.60 no.4
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• pp.248-255
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• 2023

In order to implement the autonomous navigation function, it is essential to track an object within a certain radius of the ship's route. This paper proposes the Multiple Probabilistic Data Association Filter (MPDAF), which can track multiple ships by extending Probabilistic Data Association Filter (PDAF), an existing single object tracking algorithm, using radar data obtained from real marine environments. The proposed MPDAF algorithm was developed to address the problem of tracking multiple objects in a complex environment where there can be significant uncertainty in the number and identification of objects to be tracked. Using real-world radar data provided by the German aerospace center (DLR), it has been verified that the proposed algorithm can track a large number of objects with a small position error.

• Journal of Intelligence and Information Systems
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• v.15 no.1
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• pp.51-64
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• 2009

A location tracking sensor such as GPS, INS, Radar, and optical equipments is used in tracking Maneuvering Targets with a multi-sensor, and such systems are used to track, detect, and control UAV, guided missile, and spaceship. Until now, Most of the studies related to tracking Maneuvering Targets are on fusing multiple Radars, or adding a supplementary sensor to INS and GPS. However, A study is required to change the degree of application in fusions since the system property and error property are different from sensors. In this paper, we perform the error analysis of the sensor properties by adding a ground radar to GPS and INS for improving the tracking performance by multi-sensor fusion, and suggest the tracking algorithm that improves the precision and stability by changing the sensor probability of each sensor according to the error. For evaluation, we extract the altitude values in a simulation for the trajectory of UAV and apply the suggested algorithm to carry out the performance analysis. In this study, we change the weight of the evaluated values according to the degree of error between the navigation information of each sensor to improve the precision of navigation information, and made it possible to have a strong tracking which is not affected by external purposed environmental change and disturbance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37 no.5C
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• pp.355-364
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• 2012

Target processing mechanisms are necessary to collect target information, real-time data fusion, and tactical environment recognition for cooperative engagement ability. Among these mechanisms, the target tracking starts from predicting state of speed, acceleration, and location by using sensors' measurements. However, it can be a problem to give the reliability because the measurements have a certain uncertainty. Thus, a technique which uses multiple sensors is needed to detect the target and increase the reliability. Also, data fusion technique is necessary to process the data which is provided from heterogeneous sensors for target tracking. In this paper, a target tracking algorithm is proposed based on probabilistic data association(PDA) by fusing radar and ESM sensor measurements. The radar sensor's azimuth and range measurements and the ESM sensor's bearing-only measurement are associated by the measurement fusion method. After gating associated measurements, state estimation of the target is performed by PDA filter. The simulation results show that the proposed algorithm provides improved estimation under linear and circular target motions.

• The Journal of the Acoustical Society of Korea
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• v.34 no.1
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• pp.75-81
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• 2015

In active sonar system, clutters degrade performance of target detection/tracking and overwhelm sonar operators in ASW (Antisubmarine Warfare). Conventional clutter reduction algorithms using consistency of local peaks are studied in multi-ping data and tracking filter research for active sonar was conducted. However these algorithms cannot classify target and clutters in single ping data. This paper suggests a single ping clutter reduction approach to reduce clutters in mid-frequency active sonar system using echo shape features. The algorithm performance test is conducted using real sea-trial data in heavy clutter density environment. It is confirmed that the number of clutters was reduced by about 80 % over the conventional algorithm while retaining the detection of target.

• Journal of information and communication convergence engineering
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• v.8 no.2
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• pp.145-149
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• 2010

In this paper, we propose a kinematic approach to estimating the real-time moving object. A new scheme for a mobile robot to track and capture a moving object using images of a camera is proposed. The moving object is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the active camera. Uncertainties in the position estimation caused by the point-object assumption are compensated using the Kalman filter. To generate the shortest time path to capture the moving object, the linear and angular velocities are estimated and utilized. The experimental results of tracking and capturing of the target object with the mobile robot are presented.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2796-2798
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• 2005

This Paper presents a method that a mobile robot can track persons in complex environment using particle filters. The topic of person following using mobile robot is researched in many different areas. The main problems of following a person are real time constraint, motion change of person during the tracking and occlusion with other objects. We present appearance adaptive models in a particle filter to realize robust visual tracking algorithm. Adaptive appearance model can handle occlusion with other people while target is moving.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.7
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• pp.635-641
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• 2008

In this paper, we used a laser range finder (LRF) to detect both the static and dynamic obstacles for the safe navigation of a mobile robot. LRF sensor measurements containing the information of obstacle's geometry are first processed to extract the characteristic points of the obstacle in the sensor field of view. Then the dynamic states of the characteristic points are approximated using kinematic model, which are tracked by associating the measurements with Probability Data Association Filter. Finally, the collision avoidance algorithm is developed by using fuzzy decision making algorithm depending on the states of the obstacles tracked by the proposed obstacle tracking algorithm. The performance of the proposed algorithm is evaluated through experiments with the experimental mobile robot.