• The Bulletin of The Korean Astronomical Society
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• v.41 no.2
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• pp.48.3-49
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• 2016

We have five different types of observation modes with regard to the Deep Ecliptic Patrol of the Southern Sky (DEEP-South); Opposition Census (OC) for targeted photometry, Sweet Spot Survey (S1) for discovery and orbit characterization of Atens and Atiras, Ecliptic Survey (S2) for asteroid family studies and comet census, NEOWISE follow-up (NW) for near simultaneous albedo measurements in the visible bands, and Target of Opportunity (TO) observation for follow-up either for unpredictable events or targets of special interests. Different exposures with such different modes result in a wide range of background noise level, the number of background stars and the mover's projected speed in each image. The Moving Object Detection Program (MODP) utilizes multiple mosaic images being taken for the same target fields at different epochs at the three KMTNet sites. MODP employs existing software packages such as SExtractor (Source-Extractor) and SCAMP (Software for Calibrating Astrometry and Photometry); SExtractor generates object catalogs, while SCAMP conducts precision astrometric calibration, then MODP determines if a point source is moving. This package creates animated stamp images for visual inspection with MPC reports, the latter for checking whether an object is known or unknown. We evaluate the astrometric accuracy and efficiency of MODP using the year one dataset obtained from DEEP-South operations.

• The Journal of Korean Institute of Information Technology
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• v.17 no.6
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• pp.103-114
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• 2019

Moving-target detection using frequency-modulated continuous-wave (FMCW) radar systems has recently attracted attention. Detection tasks are more challenging with noise resulting from signals reflected from strong static objects or small moving objects(clutter) within radar range. Robust Principal Component Analysis (RPCA) approach for FMCW radar to detect moving objects in noisy environments is employed in this paper. In detail, compensation and calibration are first applied to raw input signals. Then, RPCA via Gradient Descents (RPCA-GD) is adopted to model the low-rank noisy background. A novel update algorithm for RPCA is proposed to reduce the computation cost. Finally, moving-targets are localized using an Automatic Multiscale-based Peak Detection (AMPD) method. All processing steps are based on a sliding window approach. The proposed scheme shows impressive results in both processing time and accuracy in comparison to other RPCA-based approaches on various experimental scenarios.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.798-802
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• 2003

The robustness and reliability of vision algorithms is the key issue in robotic research and industrial applications. In this paper, the robust real time visual tracking in complex scene is considered. A common approach to increase robustness of a tracking system is to use different models (CAD model etc.) known a priori. Also fusion of multiple features facilitates robust detection and tracking of objects in scenes of realistic complexity. Because voting is a very simple or no model is needed for fusion, voting-based fusion of cues is applied. The approach for this algorithm is tested in a 3D Cartesian robot which tracks a toy vehicle moving along 3D rail, and the Kalman filter is used to estimate the motion parameters, namely the system state vector of moving object with unknown dynamics. Experimental results show that fusion of cues and motion estimation in a tracking system has a robust performance.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.1211-1212
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• 2008

In this paper, we propose image process modeling as a part of location based surveillance system for unauthorized target recognition and tracking in harbor, airport, military zone. For this, we compress and store background image in lower resolution and perform object extraction and motion tracking by using sobel edge detection and difference picture method between real images and a background image. In addition to, we use Independent Component Analysis Neural Network for moving target recognition. Experiments are performed for object extraction and tracking of moving targets on road by using static camera in 20m height building and it shows the robust results.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.217-220
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• 2001

In this paper, we propose a segmentation method of moving object using the spatio-temporal information in image sequences. Proposed method consists of motion detection step using difference image, region segmentation step using k-means algorithm, motion estimation step and segmenting step using intensity and motion information. Experimental results show that the method is capable of segmenting variously moving objects in image sequences.

• 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.

• The Journal of Information Technology
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• v.10 no.4
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• pp.27-39
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• 2007

Object tracking in a real time environment is one of challenging subjects in computer vision area during past couple of years. This paper proposes a method of object detection and tracking using adaptive background estimation in real time environment. To obtain a stable and adaptive background, we combine 3-frame differential method and running average single gaussian background model. Using this background model, we can successfully detect moving objects while minimizing false moving objects caused by noise. In the tracking phase, we propose a matching criteria where the weight of position and inner brightness distribution can be controlled by the size of objects. Also, we adopt a Kalman Filter to overcome the occlusion of tracked objects. By experiments, we can successfully detect and track objects in real time environment.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.6
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• pp.810-812
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• 2014

In this paper, a hagwon-bus boarding danger warning system based on computer vision is proposed to protect children from an accident causing injuries or death. Three zones are defined and different algorithms are applied to detect moving objects. In zone 1, a block-based entropy value is calculated using the absolute difference image generated by the absolute differential estimation between background image and incoming video frame. In zone 2, an effective and robust motion object tracking algorithm is performed based on the particle filter. Experimental results demonstrate the efficient and effectively of the algorithm for moving object inspection in each zone.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.2A
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• pp.157-164
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• 2002

The Mellin Transform is a well-known method to find out transformations between two overlapping images. This paper models a scene with an object moving with different velocities. Also, it analyzes the relationship of the detected correlation peak to the spectrum of the background and an object. Lastly, we investigate the optimal solution of the image registration parameters minimizing the effect of the noise from the Mellin Transform by applying the Wiener filter concept.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.5
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• pp.547-553
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• 2021

Dynamic object recognition is an important task for autonomous vehicles. Since dynamic objects exhibit a higher collision risk than static objects, our own trajectories should be planned to match the future state of moving elements in the scene. Time information such as optical flow can be used to recognize movement. Existing optical flow calculations are based only on camera sensors and are prone to misunderstanding in low light conditions. In this regard, to improve recognition performance in low-light environments, we applied a normalization filter and a correction function for Gamma Value to the input images. The low light quality improvement algorithm can be applied to confirm the more accurate detection of Object's Bounding Box for the vehicle. It was confirmed that there is an important in object recognition through image prepocessing and deep learning using YOLO.