• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.5
• /
• pp.1711-1725
• /
• 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.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.15 no.3
• /
• pp.343-349
• /
• 2012

To detect targets for autonomous navigation of unmanned ground vehicle, mounted sensors are required to work all-weather condition. In this point of view, the FMCW radar is quietly appropriate. In this paper, we present development results of target signal simulator for multi-beam type FMCW radar. A target signal simulator make pseudo target signals which simulates multiple moving targets. And we describe how to make hit information for each target in multi-beam type radar. The developed methods are utilized for target tracking device. Moreover it can be applied to similar target signal simulator.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.56 no.10
• /
• pp.1826-1835
• /
• 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 Ocean Engineering and Technology
• /
• v.28 no.5
• /
• pp.429-439
• /
• 2014

The target echoes contain information on the target such as the orientation, kinematics, and internal structure, as well as the external geometrical shape of the target. In addition, the pattern of the target echoes depends on the arrangement of the transmitters and receivers in space. Therefore, the study of the target echoes in a multi-static SONAR system can be useful for detecting and tracking submerged objects using an underwater surveillance system. For this purpose, an acoustic measuring system for multi-static target echoes was designed and tested in an acoustic water tank. Some preliminary data are presented and discussed.

• Journal of the Korea Society of Computer and Information
• /
• v.21 no.11
• /
• pp.31-38
• /
• 2016

In this paper, we propose multi-scale infrared target detection algorithm with varied filter size using integral image. Filter based target detection is widely used for small target detection, but it doesn't suit for large target detection depending on the filter size. When there are multi-scale targets on the sky background, detection filter with small filter size can not detect the whole shape of the large targe. In contrast, detection filter with large filter size doesn't suit for small target detection, but also it requires a large amount of processing time. The proposed algorithm integrates the filtering results of varied filter size for the detection of small and large targets. The proposed algorithm has good performance for both small and large target detection. Furthermore, the proposed algorithm requires a less processing time, since it use the integral image to make the mean images with different filter sizes for subtraction between the original image and the respective mean image. In addition, we propose the implementation of real-time embedded system using FPGA.

• Proceedings of the KSME Conference
• /
• 2004.11a
• /
• pp.696-701
• /
• 2004

A vehicle cruise control algorithm using an Interacting Multiple Model (IMM)-based Multi-Target Tracking (MTT) method has been presented in this paper. The vehicle cruise control algorithm consists of three parts; track estimator using IMM-Probabilistic Data Association Filter (PDAF), a primary target vehicle determination algorithm and a single-target adaptive cruise control algorithm. Three motion models; uniform motion, lane-change motion and acceleration motion, have been adopted to distinguish large lateral motions from longitudinal motions. The models have been validated using simulated and experimental data. The improvement in the state estimation performance when using three models is verified in target tracking simulations. The performance and safety benefits of a multi-model-based MTT-ACC system is investigated via simulations using real driving radar sensor data. These simulations show system response that is more realistic and reflective of actual human driving behavior.

• Journal of Korean Society for Quality Management
• /
• v.41 no.3
• /
• pp.413-421
• /
• 2013

Purpose: System availability and life cycle cost are often used to evaluate the system performance and is influenced by the operation and maintenance characteristic. In this paper, we propose the method to improve life cycle cost and satisfy the target availability through redundancy allocation. Methods: We consider the redundancy is available at all items in multi level system. Thus, we assume that sub-assembly, module, components can be duplicated. Simulation and genetic algorithm are employed to optimize redundancy allocation. Results: Target availability is higher, the life cycle cost is increased. In addition, the items for redundancy are selected at higher level in multi level system if target availability is higher. Conclusion: We could know that target availability affects the duplication number of items and the selection of redundancy items. For further study, we will consider new optimization algorithms to compare with the proposed GA algorithm and improve optimization performance.

• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.8
• /
• pp.783-789
• /
• 2011

Target tracking performance improvement using multi-sensor data fusion is a challenging work. However, biases in the measurements should be removed before various data fusion techniques are applied. In this paper, a bias removing algorithm using measurement data from multi-radar tracking systems is proposed and evaluated by computer simulation. To predict bias estimation performance in various geometric relations between the radar systems and target, a system observability index is proposed and tested via computer simulation results. It is also studied that target tracking which utilizes multi-sensor data fusion with bias-removed measurements results in better performance.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.1
• /
• pp.7-13
• /
• 2015

Unmanned air vehicles (UAVs) are getting popular not only as a private usage for the aerial photograph but military usage for the surveillance, reconnaissance and supply missions. For an UAV to successfully achieve these kind of missions, geolocation (localization) must be implied to track an interested target or fly by reference. In this research, we adopted multi-sensor fusion (MSF) algorithm to increase the accuracy of the geolocation and verified the algorithm using two multicopter UAVs. One UAV is equipped with an optical camera, and another UAV is equipped with an optical camera and a laser range finder. Throughout the experiment, we have obtained measurements about a fixed ground target and estimated the target position by a series of coordinate transformations and sequential Kalman filter. The result showed that the MSF has better performance in estimating target location than the case of using single sensor. Moreover, the experimental result implied that multi-sensor geolocation algorithm is able to have further improvements in localization accuracy and feasibility of other complicated applications such as moving target tracking and multiple target tracking.

• Journal of the Institute of Convergence Signal Processing
• /
• v.12 no.2
• /
• pp.76-82
• /
• 2011

In this paper, we propose the technique to detect the location and size of the small target in multi-resolution image using cubic facet model. The input image is reduced by the multi-resolution and we obtain the multi-resolution images. We apply the facet model and the local maxima conditions to the multi-resolution images of each level. And then, we detect the location of the small target. We estimate that the location at the maximum of the $D_2$ which means the local maxima value of the facet model in the multi-resolution images is the location of the small target. We can detect the small target of the various size about the multi-resolution images of each level. In this paper, we experimented in the various infrared images with the small target. The method using the typical facet model applies a mask. However, the proposed method applies a mask in the multi-resolution images. We verified to vary the mask size and differ the size of the small target. The proposed algorithm can detect the location and size of the small target.