• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.3
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• pp.70-77
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• 2017

Recently, Internet of Things (IoT) and deep learning techniques have affected video surveillance systems in various ways. The surveillance features that perform detection, tracking, and classification of specific objects in Closed Circuit Television (CCTV) video are becoming more intelligent. This paper presents real-time algorithm that can run in a PC environment using only a low power CPU. Traditional tracking algorithms combine background modeling using the Gaussian Mixture Model (GMM), Hungarian algorithm, and a Kalman filter; they have relatively low complexity but high detection errors. To supplement this, deep learning technology was used, which can be trained from a large amounts of data. In particular, an SRGB(Sequential RGB)-3 Layer CNN was used on tracked objects to emphasize the features of moving people. Performance evaluation comparing the proposed algorithm with existing ones using HOG and SVM showed move-in and move-out error rate reductions by 7.6 % and 9.0 %, respectively.

• Journal of Astronomy and Space Sciences
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• v.34 no.4
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• pp.303-314
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• 2017

With increased human activity in space, the risk of re-entry and collision between space objects is constantly increasing. Hence, the need for space situational awareness (SSA) programs has been acknowledged by many experienced space agencies. Optical and radar sensors, which enable the surveillance and tracking of space objects, are the most important technical components of SSA systems. In particular, combinations of radar systems and optical sensor networks play an outstanding role in SSA programs. At present, Korea operates the optical wide field patrol network (OWL-Net), the only optical system for tracking space objects. However, due to their dependence on weather conditions and observation time, it is not reasonable to use optical systems alone for SSA initiatives, as they have limited operational availability. Therefore, the strategies for developing radar systems should be considered for an efficient SSA system using currently available technology. The purpose of this paper is to analyze the performance of a radar system in detecting and tracking space objects. With the radar system investigated, the minimum sensitivity is defined as detection of a $1-m^2$ radar cross section (RCS) at an altitude of 2,000 km, with operating frequencies in the L, S, C, X or Ku-band. The results of power budget analysis showed that the maximum detection range of 2,000 km, which includes the low earth orbit (LEO) environment, can be achieved with a transmission power of 900 kW, transmit and receive antenna gains of 40 dB and 43 dB, respectively, a pulse width of 2 ms, and a signal processing gain of 13.3 dB, at a frequency of 1.3 GHz. We defined the key parameters of the radar following a performance analysis of the system. This research can thus provide guidelines for the conceptual design of radar systems for national SSA initiatives.

• Smart Media Journal
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• v.12 no.7
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• pp.18-26
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• 2023

In this study, we propose a cow behavior recognition system based on YOLOX and OC-SORT. YOLO X detects targets in real-time and provides information on cow location and behavior. The OC-SORT module tracks cows in the video and assigns unique IDs. The quantitative analysis module analyzes the behavior and location information of cows. Experimental results show that our system demonstrates high accuracy and precision in target detection and tracking. The average precision (AP) of YOLOX was 82.2%, the average recall (AR) was 85.5%, the number of parameters was 54.15M, and the computation was 194.16GFLOPs. OC-SORT was able to maintain high-precision real-time target tracking in complex environments and occlusion situations. By analyzing changes in cow movement and frequency of mounting behavior, our system can help more accurately discern the estrus behavior of cows.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1224-1242
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• 2018

Aiming at motion analysis and compensation, it is essential to conduct motion detection with images. However, motion detection and tracking from low-altitude images obtained from an unmanned aerial system may pose many challenges due to degraded image quality caused by platform motion, image instability and illumination fluctuation. This research tackles these challenges by proposing a modified joint transform correlation algorithm which includes two preprocessing strategies. In spatial domain, a modified fuzzy edge detection method is proposed for preprocessing the input images. In frequency domain, to eliminate the disturbance of self-correlation items, the cross-correlation items are extracted from joint power spectrum output plane. The effectiveness and accuracy of the algorithm has been tested and evaluated by both simulation and real datasets in this research. The simulation experiments show that the proposed approach can derive satisfactory peaks of cross-correlation and achieve detection accuracy of displacement vectors with no more than 0.03pixel for image pairs with displacement smaller than 20pixels, when addition of image motion blurring in the range of 0~10pixel and 0.002variance of additive Gaussian noise. Moreover,this paper proposes quantitative analysis approach using tri-image pairs from real datasets and the experimental results show that detection accuracy can be achieved with sub-pixel level even if the sampling frequency can only attain 50 frames per second.

• Journal of the Korea Society of Computer and Information
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• v.14 no.11
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• pp.179-186
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• 2009

In this paper, we propose a head detection method based on vertical and horizontal pixel histogram analysis in order to overcome drawbacks of the previous head detection approach using Haar-like feature-based face detection. In the proposed method, we create the vertical and horizontal foreground pixel histogram images from the background subtraction image, which represent the number of foreground pixels in the same vertical or horizontal position. Then we extract feature points of a head region by applying Harris corner detection method to the foreground pixel histogram images and by analyzing corner points. The proposal method shows robust head detection results even in the face image covering forelock by hairs or the back view image in which the previous approaches cannot detect the head regions.

• Journal of Korean Society for Geospatial Information Science
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• v.16 no.2
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• pp.49-56
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• 2008

Image registration is one of the critical techniques of image mosaic which has many applications such as generating panoramas, video monitoring, image rendering and reconstruction, etc. The fundamental tasks of image registration are point features extraction and tracking which take much computation time. KLT(Kanade-Lucas-Tomasi) feature tracker has proposed for extracting and tracking features through image sequences. The aim of this study is to demonstrate the usage of effective and robust KLT feature detector and tracker for an image registration using the sequence image frames captured by UAV video camera. In result, by using iterative implementation of the KLT tracker, the features extracted from the first frame of image sequences could be successfully tracked through all frames. The process of feature tracking in the various frames with rotation, translation and small scaling could be improved by a careful choice of the process condition and KLT pyramid implementation.

• Journal of Digital Contents Society
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• v.14 no.4
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• pp.589-596
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• 2013

With the increase of social crime rate, the interest on the intelligent security system is also growing. This paper proposes a detection system of monitoring whether abnormal behavior is being carried in the images captured using CCTV. After detection of an object via subtraction from background image and morpholgy, this system extracts an abnormal behavior by each object's feature information and its trajectory. When an object is loitering for a while in CCTV images, this system considers the loitering as an abnormal behavior and sends the alarm signal to the control center to facilitate prevention in advance. Especially, this research aims at detecting a loitoring act among various abnormal behaviors and also extends to the detection whether an incoming object is identical to one of inactive objects out of image.

• Journal of the Institute of Convergence Signal Processing
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• v.16 no.2
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• pp.44-49
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• 2015

When the scene has less texture or when camera pose largely changes, the existing texture-based feature tracking methods are not reliable. Topology-based feature description methods, which use the geometric relationship between features such as LLAH, is a good alternative. However, they require feature detection methods with high performance. As a basic study on developing an effective feature detection method for topology-based feature description, this paper aims at examining their applicability to topology-based feature description by analyzing the repeatability of several feature detection methods that are included in the OpenCV library. Experimental results show that FAST outperforms the others.

• Journal of Power Electronics
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• v.16 no.4
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• pp.1526-1540
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• 2016

This work mainly discusses an accurate and fast islanding detection based on fractional wavelet packet transform (FRWPT)for multibus microgrid systems. The proposed protection scheme uses combined desirable features retrieved from discrete fractional Fourier transform (FRFT) and wavelet packet transform (WPT) techniques, which provides precise time-frequency information on minute perturbation signals introduced in the system. Moreover, this study focuses on the design of decoupling control with a distributed controller based on state feedback for the efficient operation of microgrid systems that are transitioning from the grid-connected mode to the islanded mode. An IEEE 9-bus test system with inverter based distributed generation (DG) units is considered for islanding assessment and smooth operation. Finally, tracking errors are greatly reduced with stability improvement based on the proposed controller. FRWPT based islanding detection is demonstrated via a time domain simulation of the system. Simulated results show an improvement in system stability with the application of the proposed controller and accurate islanding detection based on the FRWPT technique in comparison with the results obtained by applying the wavelet transform (WT) and WPT.

• Journal of Advanced Information Technology and Convergence
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• v.10 no.2
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• pp.167-175
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• 2020

In this paper, we propose a traffic accident detection on vehicle-mounted camera. In the proposed method, the minimum bounding box coordinates the central coordinates on the bird's eye view and motion vectors of each vehicle object, and ego-motions of the vehicle equipped with dash-cam are extracted from the dash-cam video. By using extracted 4 kinds features as the input of Bi-LSTM (bidirectional LSTM), the accident probability (score) is predicted. To investigate the effect of each input feature on the probability of an accident, we analyze the performance of the detection the case of using a single feature input and the case of using a combination of features as input, respectively. And in these two cases, different detection models are defined and used. Bi-LSTM is used as a cascade, especially when a combination of the features is used as input. The proposed method shows 76.1% precision and 75.6% recall, which is superior to our previous work.