• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.2
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• pp.23-29
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• 2014

The advanced traffic management system of intelligent transport systems automates the related traffic tasks such as vehicle speed, traffic volume and traffic incidents through the improved infrastructures like high definition cameras, high-performance radar sensors. For the safety of road users, especially, the automated incident detection and secondary accident prevention system is required. Normally, CCTV based image object detection and radar based object detection is used in this system. In this paper, we proposed the algorithm for real time highway incident detection system using multi surveillance cameras to mosaic video and track accurately the moving object that taken from different angles by background modeling. We confirmed through experiments that the video detection can supplement the short-range shaded area and the long-range detection limit of radar. In addition, the video detection has better classification features in daytime detection excluding the bad weather condition.

• The Korean Journal of Physiology and Pharmacology
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• v.14 no.6
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• pp.413-417
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• 2010

To understand the function of edges in perception of moving objects, we defined four questions to answer. Is the focus point in visual motion detection of a moving object: (1) the body or the edge of the object, (2) the leading edge or trailing edge of the object, (3) different in scotopic, mesopic and photopic luminance levels, or (4) different for colored objects? We measured the Optomotor Response (OMR) and Edge Triggering Response (ETR) of goldfish. We used a square and sine wave patterns with black and red stripes and a square wave pattern with black and grey stripes to generate OMR's and ETR's in the goldfish. When we used black and red stripes, the black leading edges stimulated an ETR under scotopic conditions, red leading edges stimulated an ETR under photopic conditions, and both black and red leading edges stimulated an ETR under mesopic luminance levels. For black and gray stripes, only black leading edges stimulated an ETR in all three light illumination levels. We observed less OMR and ETR results using the sine wave pattern compared to using the square wave pattern. From these results, we deduced that the goldfish tend to prefer tracking the leading edge of the pattern. The goldfish can also detect the color of the moving pattern under photopic luminance conditions. We decided that ETR is an intriguing factor in OMR, and is suitable as a method of behavioral measurement in visual system research.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.1
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• pp.158-164
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• 2009

The real time security system using web camera shall correspond in commensurate with it in real time through classifying moving object and analyzing the behavior. But, as to the detection of moving object in real time image through a camera, it is difficult to detect movement correctly according to the change of unnecessary noises, lighting conditions and screened phenomenon. This paper proposes real time security system by dual camera and ultrasonic sensor, a method of advanced detection in order to detect correct movement of specific object. That is, we could improve the tracing characteristics by using ultrasonic sensor as measurement factor of changed position and verify through experiments that the information interchanged between camera upwards and in front of it have effect on tracing a specific object continuously. The results of the experiment show that recognition rate of object was 97.4% and the correct tracing could be done lastingly in a phenomena of screening object.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.1
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• pp.69-76
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• 2010

The segmentation of moving object in video sequence is a core technique of intelligent image processing system such as video surveillance, traffic monitoring and human tracking. A typical method to segment a moving region from the background is the background subtraction. The steps of background subtraction involve calculating a reference image, subtracting new frame from reference image and then thresholding the subtracted result. One of famous background modeling is Gaussian mixture model (GMM). Even though the method is known efficient and exact, GMM suffers from a problem that includes false pixels in ROI (region of interest), specifically shadow pixels. These false pixels cause fail of the post-processing tasks such as tracking and object recognition. This paper presents a method for removing false pixels included in ROT. First, we subdivide a ROI by using shape characteristics of detected objects. Then, a method is proposed to classify pixels from using histogram characteristic and comparing difference of energy that converts the color value of pixel into grayscale value, in order to estimate whether the pixels belong to moving object area or shadow area. The method is applied to real video sequence and the performance is verified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5496-5521
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• 2018

Object detection and tracking is the basic capability of mobile robots to achieve natural human-robot interaction. In this paper, an object tracking system of mobile robot is designed and validated using improved multiple instance learning algorithm. The improved multiple instance learning algorithm which prevents model drift significantly. Secondly, in order to improve the capability of classifiers, an active sample selection strategy is proposed by optimizing a bag Fisher information function instead of the bag likelihood function, which dynamically chooses most discriminative samples for classifier training. Furthermore, we integrate the co-training criterion into algorithm to update the appearance model accurately and avoid error accumulation. Finally, we evaluate our system on challenging sequences and an indoor environment in a laboratory. And the experiment results demonstrate that the proposed methods can stably and robustly track moving object.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2430-2436
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• 2013

This study suggests a method of detecting the still object, which becomes a cause of danger within the crossroad. The Inverse Perspective Transform was performed in order to make the object size consistent by being inputted the real-time image from CCTV that is installed within the crossroad. It established the detection area in the image with the perspective transform and generated the adaptative background image with the use of the moving information on object. The detection of the stop object was detected the candidate region of the stop object by using the background-image differential method. To grasp the appearance of truth on the detected candidate region, a method is proposed that uses the gradient information on image and EHD(Edge Histogram Descriptor). To examine performance of the suggested algorithm, it experimented by storing the images in the commuting time and the daytime through DVR, which is installed on the cross street. As a result of experiment, it could efficiently detect the stop vehicle within the detection region inside the crossroad. The processing speed is shown in 13~18 frame per second according to the area of the detection region, thereby being judged to likely have no problem about the real-time processing.

• Journal of information and communication convergence engineering
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• v.10 no.3
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• pp.307-314
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• 2012

This paper reports on the design of an object tracker that utilizes a family of unscented Kalman filters, one for each tracked object. This is a more efficient design than having one unscented Kalman filter for the family of all moving objects. The performance of the designed and implemented filter is demonstrated by using simulated movements, and also for object movements in 2D and 3D space.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.12
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• pp.1267-1279
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• 2014

In this paper, we suggest the development of object detection systems for the safety of the ship through the study of the properties of short-range radar. Many of the short-range radars developed for special purpose like cars has cheaper price advantages but it is not proper to every application. In order to overcome such obstacles we need to analysis data from experiments in various environments and feature analysis of the device is essential. Also, the data clustering algorithms to display correct classified moving objects is necessary. In this paper we propose the advanced fast moving object detection system using short range radars with better detection accuracy. And we proposed a clustering algorithm using the value of the RCS and the speed and trajectory information of the radar data that are reflected.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.12
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• pp.2213-2220
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• 2017

This paper proposes an efficient approach for hardware implementation of moving object detection (MOD) processor using effective Gaussian mixture learning (EGML)-based background subtraction method. Arithmetic units used in background generation were implemented using LUT-based approximation to reduce hardware complexity. Hardware resources used for both background subtraction and Gaussian probability density calculation were shared. The MOD processor was verified by FPGA-in-the-loop simulation using MATLAB/Simulink. The MOD performance was evaluated by using six types of video defined in IEEE CDW-2014 dataset, which resulted the average of recall value of 0.7700, the average of precision value of 0.7170, and the average of F-measure value of 0.7293. The MOD processor was implemented with 882 slices and block RAM of $146{\times}36kbits$ on Virtex5 FPGA, resulting in 60% hardware reduction compared to conventional design based on EGML. It was estimated that the MOD processor could operate with 75 MHz clock, resulting in real-time processing of $800{\times}600$ video with a frame rate of 39 fps.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.260-261
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• 2008

이동 물체 탐지(Object Detection) 기법은 대부분의 감시 시스템에서 가장 초기 단계로서, 이후에 물체 추적(Object Tracking) 및 물체 식별(Object Classification) 등의 지능 알고리듬에 입력으로 사용된다. 따라서 물체의 윤곽의 변화 없이 최대한 정교하게 이동 물체 영역 맵을 생성하는 것이 물체 탐지의 가장 중요한 요소가 된다. 카메라가 고정되어 있는 경우에는 현재 들어오는 영상에 대한 확률적 배경 모델을 생성할 수 있지만, 팬틸트 카메라와 같이 영상의 좌표가 변하는 환경에서는 배경 모델도 계속 변하기 때문에 기존의 배경 모델을 그대로 사용할 수 없다. 본 논문에서는 팬틸트 카메라와 같이 동적인 카메라에서 이동 물체 탐지를 위해, 국소 특징점(Local Feature)를 통해 카메라의 움직임을 판단하여 연속되는 영상간의 변환 행렬(Transformation Matrix)를 구하고 하고, 확률적 배경 모델링을 통한 이동 물체 탐지 기법을 제안한다. 자제 촬영한 이동 카메라 실험영상을 통해서 이 알고리듬이 동적 배경에서도 매우 강인하게 동작하는 것을 검증하였다.