• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.455-467
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• 2017

When performing target detection using hyperspectral imagery, a feature extraction process is necessary to solve the problem of redundancy of adjacent spectral bands and the problem of a large amount of calculation due to high dimensional data. This study proposes a new band selection method using the $L_{2,1}$-norm regression model to apply the feature selection technique in the machine learning field to the hyperspectral band selection. In order to analyze the performance of the proposed band selection technique, we collected the hyperspectral imagery and these were used to analyze the performance of target detection with band selection. The Adaptive Cosine Estimator (ACE) detection performance is maintained or improved when the number of bands is reduced from 164 to about 30 to 40 bands in the 350 nm to 2500 nm wavelength band. Experimental results show that the proposed band selection technique extracts bands that are effective for detection in hyperspectral images and can reduce the size of the data without reducing the performance, which can help improve the processing speed of real-time target detection system in the future.

• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.5
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• pp.613-621
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• 2013

This paper presents the direction finding proximity fuze sensor using the clutter rejection method and the adaptive target detection algorithm for anti-air missiles. To remove effects by clutter and detect a target accurately, the clutter rejection method of Legendre sequence with BPSK(Bi phase Shift Keying) modulation has been proposed and the Doppler signal which has cross correlation characteristics is obtained from reflected target signals. Considering the change of the Doppler signal, the adaptive target detection algorithm has been developed and the direction finding algorithm has been fulfilled by comparing received powers from adjacent three receiving antennas. The encounter simulation test apparatus was made to collect and analyze reflected signal and test results showed that the -10 dBsm target was detected over 10 meters and the target with mesh clutter was detected and direction was distinguished definitely.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.6
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• pp.1166-1191
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• 2011

This paper presents an adaptive active tracking system with sector based scanning for a single PTZ camera. Dividing sectors on an image reduces the search space to shorten selection time so that the system can cover many targets. Upon the selection of a target, the system estimates the target trajectory to predict the zooming location with a finite amount of time for camera movement. Advanced estimation techniques using probabilistic reason suffer from the unknown object dynamics and the inaccurate estimation compromises the zooming level to prevent tracking failure. The proposed system uses the simple piecewise estimation with a few frames to cope with fast moving objects and/or slow camera movements. The target is tracked in multiple steps and the zooming time for each step is determined by maximizing the zooming level within the expected variation of object velocity and detection. The number of zooming steps is adaptively determined according to target speed. In addition, the iterative estimation of a zooming location with camera movement time compensates for the target prediction error due to the difference between speeds of a target and a camera. The effectiveness of the proposed method is validated by simulations and real time experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.5112-5128
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• 2016

Tracking human body is an important problem in computer vision field. Tracking failures caused by occlusion can lead to wrong rectification of the target position. In this paper, a robust human tracking algorithm is proposed to address the problem of occlusion, rotation and improve the tracking accuracy. It is based on Tracking-Learning-Detection framework. The key auxiliary information is used in the framework which motivated by the fact that a tracking target is usually embedded in the context that provides useful information. First, face localization method is utilized to find key face location information. Second, the relative position relationship is established between the auxiliary information and the target location. With the relevant model, the key face information will get the current target position when a target has disappeared. Thus, the target can be stably tracked even when it is partially or fully occluded. Experiments are conducted in various challenging videos. In conjunction with online update, the results demonstrate that the proposed method outperforms the traditional TLD algorithm, and it has a relatively better tracking performance than other state-of-the-art methods.

• Journal of Korea Multimedia Society
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• v.24 no.3
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• pp.416-422
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• 2021

Due to the large differences in human activity within classes, the large similarity between classes, and the problems of visual angle and occlusion, it is difficult to extract features manually, and the detection rate of human behavior is low. In order to better solve these problems, an improved Faster R-CNN-based detection algorithm is proposed in this paper. It achieves multi-object recognition and localization through a second-order detection network, and replaces the original feature extraction module with Dense-Net, which can fuse multi-level feature information, increase network depth and avoid disappearance of network gradients. Meanwhile, the proposal merging strategy is improved with Soft-NMS, where an attenuation function is designed to replace the conventional NMS algorithm, thereby avoiding missed detection of adjacent or overlapping objects, and enhancing the network detection accuracy under multiple objects. During the experiment, the improved Faster R-CNN method in this article has 84.7% target detection result, which is improved compared to other methods, which proves that the target recognition method has significant advantages and potential.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.174-177
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• 1995

Detecting a small threat object either fast moving or floating on shallow water presents a formidable challenge to shipboard sensor systems, which must determine whether or not to launch defensive weapons in a timely manner. An integrated multisensor concept is envisioned wherein the combined use of active and passive sensor is employed for the detection of short duration targets in dense ocean surface clutter to maximize detection range. The objective is to develop multisensor integration techniques that operate on detection data prior to track formation while simultaneously fusing contacts to tracks. In the system concept, detections from a low grazing angle search radar render designations to a sensor-search infrared sensor for target classification which in turn designates an active electro-optical sensor for sector search and target verification.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.4
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• pp.700-709
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• 2011

In this paper, we propose a novel method which can detect ordnance firing events in IR images. The proposed algorithm is comprised of effective target detection and robust clutter rejection methods based on the temporalspatial cooperative filter. And additional clutter reduction is performed based on the proposed two features, NTFF (Number of Temporal Filter Frames) and SNS(Size-Normalized Signal). Experimental results show the effectiveness and feasibilities of our proposed algorithm.

• ETRI Journal
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• v.16 no.2
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• pp.15-25
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• 1994

In this paper we present a new formula which can predict the exact detection probability of a generalized order statistics (GOS) constant false alarm rate (DFAR) detector for a partially correlated Rayleigh target model (0 < $ \rho$< 1) in a closed form, where $\rho$ is the correlation coefficient between returned pulses. By simply substituting a set of specific coefficient into the derived formula, one can obtain the detection probability of any kind of CFAR detector. Detectors may include the order statistics CFAR detector, the censored mean level detector, and the trimmed mean CFAR detector, but are not necessarily restricted to them. The numerical result for the first order Markov correlation model as applied to some of the detectors shows that as $\rho$ increases from zero to one, higher signal-to-noise ratio is required to achieve the same detection probability.

• International Journal of Aeronautical and Space Sciences
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• v.13 no.1
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• pp.99-105
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• 2012

Track-before-detect techniques based on dynamic programming have provided solutions for detecting targets from a sequence of images. In its application to airborne threat detection, dynamic programming solutions should take into account the distinguishable properties of objects in a collision course. This paper describes the development of a new track scoring function that accumulates scores for airborne targets in Bayesian framework. Numerical results show that the proposed scoring function has slightly better detection capabilities.

• Journal of Veterinary Clinics
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• v.15 no.1
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• pp.140-145
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• 1998

The present study was carried out for the rapid and accurate detection of Anaplasma marginale in cattle using Polymerase Chain Reaction. One pair of primer, BAP-2 and AL34S, were designed to amplify a 409 Up fragment of the A marginale membrane surface protein encoding beta($msp{\beta}l$) gene with a hilly sensitive and specific PCR. A marginale isolated from naturally infected calf in Chonbuk area were used to obtain target genomic DNA for PCR. This study showed that a 409 bp of $msp{\beta}l$ gene fragment could be detected as little as 15 fg of purified A marginale genomic DNA. The amplified fragment with PCR was checked for the identification of $msp{\beta}l$ gene by enzyme restriction and sequencing. Also, the target DNA extracted directly from blood were used in the PCR reactions without prior purification to shorten the detection time. The PCR in the present study was considered convenient and rapid method for the detection of A marginale in whole blood of infected cattle.