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

• 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 the Korea Institute of Military Science and Technology
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• v.10 no.4
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• pp.30-37
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• 2007

In this paper, Water Entry Point Selection Algorithm(WEPSA) for selecting an optimal Water Entry Point of anti-submarine missiles which maximizes Detection Probability about a given target was investigated. WEPSA is a method which decides the position of an optimal Water Entry Point with calculating the target Detection Probability of a torpedo in the whole domain which centered by the target, performing the Monte-Carlo Simulations which include errors for the target informations and for weapon delivery. We can decide an optimal Water Entry Point of anti-submarine missiles which maximizes Detection Probability about a given target with WEPSA, if we get target informations about target range, target bearing, target speed and target course from Combat Systems.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.677-680
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• 2003

Perceptual landmarks are an effective solution for a mobile robot realizing steady and reliable long distance navigation. But the prerequisite is those landmarks must be detected and recognized robustly at a higher speed under various lighting conditions. This made image processing more complicated so that its speed and reliability can not be both satisfied at the same time. Color based target detection technique can separate target color regions from non-target color regions in an image with a faster speed, and better results were obtained only under good lighting conditions. Moreover, in the case that there are other things with a target color, we have to consider other target features to tell apart the target from them. Such thing always happens when we detect a target with its single character. On the other hand, we can generally search for only one target for each time so that we can not make use of landmarks efficiently, especially when we want to make more landmarks work together. In this paper, by making use of the moment invariants of each landmark, we can not only search specified target from separated color region but also find multi-target at the same time if necessary. This made the finite landmarks carry on more functions. Because moment invariants were easily used with some low level image processing techniques, such as color based target detection and gradient runs based target detection etc, and moment invariants are more reliable features of each target, the ratio of target detection were improved. Some necessary experiments were carried on to verify its robustness and efficiency of this method.

• International Journal of Aeronautical and Space Sciences
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• v.9 no.2
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• pp.114-120
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• 2008

In the vision-based recovery phase, a terminal guidance for the blended-wing UAV requires visual information of high accuracy. This paper presents the light source target design and detection algorithm for vision-based UAV recovery. We propose a recovery target design with red and green LEDs. This frame provides the relative position between the target and the UAV. The target detection algorithm includes HSV-based segmentation, morphology, and blob processing. These techniques are employed to give efficient detection results in day and night net recovery operations. The performance of the proposed target design and detection algorithm are evaluated through ground-based experiments.

• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.55-61
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• 2017

In this paper, we propose a novel tracking method using target separation and detection that are based on discriminative correlation filter (DCF), which is studied a lot recently. 'Retainability' is one of the most important factor of tracking. There are some factors making retainability of tracking worse. Especially, fast movement and occlusion of a target frequently occur in image data, and when it happens, it would make target lost. As a result, the tracking cannot be retained. For maintaining a robust tracking, in this paper, separation of a target is used so that normal tracking is maintained even though some part of a target is occluded. The detection algorithm is executed and find new location of the target when the target gets out of tracking range due to occlusion of whole part of a target or fast movement speed of a target. A variety of experiments with various image data sets are conducted. The algorithm proposed in this paper showed better performance than other conventional algorithms when fast movement and occlusion of a target occur.

• International Journal of Aeronautical and Space Sciences
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• v.6 no.1
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• pp.8-16
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• 2005

This paper describes target detection performance of millimeter wave radar that exits on non-stationary target detection schemes in the ground clutter conditions. The comparison of various CFAR process schemes such as CA(Cell-Average)-CFAR, GO(Greatest Of)/SO(Smallest Of)-CFAR, and OS(Order Statistics)-CFAR performance are applied. Using matlab software, we show the performance and loss between target detection probability and signal to noise ratio. This paper concludes the OS-CFAR process performance is better than any others and satisfies the optimal detection probability without loss of detection in the homogeneous clutter, When range bins increase.

• Journal of Korea Society of Industrial Information Systems
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• v.22 no.2
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• pp.1-13
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• 2017

In This Paper, we find the Target Candidate Region and the Location of the Candidate Region by Performing the Morphological Difference Calculation and Pixel Labeling for Robust Small Target Detection in Infrared Image with low SNR. Conventional Target Detection Methods based on Morphology Algorithms are low in Detection Accuracy due to their Vulnerability to Clutter in Infrared Images. To Address the Problem, Target Signal Enhancement and Background Clutter Suppression are Achieved Simultaneously by Combining Moravec Algorithm and LCM (Local Contrast Measure) Algorithm to Classify the Target and Noise in the Candidate Region. In Addition, the Proposed Algorithm can Efficiently Detect Multiple Targets by Solving the Problem of Limited Detection of a Single Target in the Target Detection method using the Morphology Operation and the Gaussian Distance Function Which were Developed for Real time Target Detection.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.7-14
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• 2006

Detection and identification of targets from remotely sensed imagery are of great interest for civilian and military application. This paper presents an algorithm for target detection in high spatial resolution imagery based on the spectral and the dimensional characteristics of the reference target. In this algorithm, the spectral and the dimensional information of the reference target is extracted automatically from the sample image of the reference target. Then in the entire image, the candidate target pixels are extracted based on the spectral characteristics of the reference target. Finally, groups of candidate pixels which form isolated spatial objects of similar size to that of the reference target are extracted as detected targets. The experimental test results showed that even though the algorithm detected spatial objects which has different shape as targets if the spectral and the dimensional characteristics are similar to that of the reference target, it could detect 97.5% of the targets in the image. Using hyperspectral image and utilizing the shape information are expected to increase the performance of the proposed algorithm.

• Korean Journal of Remote Sensing
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• v.22 no.1
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• pp.41-47
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• 2006

Detection and identification of targets from remotely sensed imagery are of great interest for civilian and military application. This paper presents an algorithm for target detection in high spatial resolution imagery based on the spectral and the dimensional characteristics of the reference target. In this algorithm, the spectral and the dimensional information of the reference target is extracted automatically from the sample image of the reference target. Then in the entire image, the candidate target pixels are extracted based on the spectral characteristics of the reference target. Finally, groups of candidate pixels which form isolated spatial objects of similar size to that of the reference target are extracted as detected targets. The experimental test results showed that even though the algorithm detected spatial objects which has different shape as targets if the spectral and the dimensional characteristics are similar to that of the reference target, it could detect 97.5% of the targets in the image. Using hyperspectral image and utilizing the shape information are expected to increase the performance of the proposed algorithm.