• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.5
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• pp.191-198
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• 2013

This paper proposes detection of view reversal in a stereo video using depth map and motion vector information. We obtain a depth map by using a stereo matching and divide the input image into foreground and background. Next, we obtain a motion vector field by using a motion estimation. In general, an occluded region is in background when foreground goes toward the adjacent background or the background goes toward the adjacent foreground. But, we will face with the change of foreground and background because their depths also change when view reversal occurs. Therefore, we can detect the view reversal in stereo videos by using the observation that the foreground goes toward the adjacent background or the background goes toward the adjacent foreground. The experimental results show that the proposed algorithm achieves good detection rate when the background region is sufficiently occluded by the moving foreground.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.11
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• pp.48-56
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• 1998

In this paper, a moving object detection and face region extraction algorithm which can be used in video monitoring systems is presented. The proposed algorithm is composed of two stages. In the first stage, each frame of an input video sequence is analyzed using three measures which are based on image pixel difference. If the current frame contains moving objects, their skin regions are extracted using color and frame difference information in the second stage. Since the proposed algorithm does not rely on computationally expensive features like optical flow, it is well suited for real-time applications. Experimental results tested on various sequences have shown the robustness of the proposed algorithm.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.85-92
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• 2017

Recently, in the field of video surveillance, a Deep Learning based learning method has been applied to a method of detecting a moving person in a video and analyzing the behavior of a detected person. The human activity recognition, which is one of the fields this intelligent image analysis technology, detects the object and goes through the process of detecting the body keypoint to recognize the behavior of the detected object. In this paper, we propose a method for Body Keypoint Localization based on Object Detection using RGB-D information. First, the moving object is segmented and detected from the background using color information and depth information generated by the two cameras. The input image generated by rescaling the detected object region using RGB-D information is applied to Convolutional Pose Machines for one person's pose estimation. CPM are used to generate Belief Maps for 14 body parts per person and to detect body keypoints based on Belief Maps. This method provides an accurate region for objects to detect keypoints an can be extended from single Body Keypoint Localization to multiple Body Keypoint Localization through the integration of individual Body Keypoint Localization. In the future, it is possible to generate a model for human pose estimation using the detected keypoints and contribute to the field of human activity recognition.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.48 no.12
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• pp.54-61
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• 2011

We propose a surveillance-alert system based on optimal placements of PDR(Pulsed Doppler Radar) sensors in USN. By using the detection information of moving target from PDR sensor and by considering the covered detection region and geographical property of the strategic area, three optimal placements of sensors are proposed. The proposed placement are named as the grid type, the linear type and the zigzag type. Also, the surveillance alert system based on three sensor placements are developed. The alert level of the proposed surveillance-alert system are 'Perception', 'Caution', 'Warning' and 'Danger' which are decided by the distance change between the moving targets and the command post. The performace of the developed system is verified via outdoor experiments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1537-1545
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• 2016

In real time video sequence, object segmentation and tracking method are actively applied in various application tasks, such as surveillance system, mobile robots, augmented reality. This paper propose a robust object tracking method. The background models are constructed by learning the initial part of each video sequences. After that, the moving objects are detected via object segmentation by using background subtraction method. The region of detected objects are continuously tracked by using the HSV color histogram with particle filter. The proposed segmentation method is superior to average background model in term of moving object detection. In addition, the proposed tracking method provide a continuous tracking result even in the case that multiple objects are existed with similar color, and severe occlusion are occurred with multiple objects. The experiment results provided with 85.9 % of average object overlapping rate and 96.3% of average object tracking rate using two video sequences.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.8
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• pp.87-95
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• 2007

In this paper, we analyzed the mr detection and the stability of the object tracking system by an adaptive stereo object hacking using region-based MAD(Mean Absolute Difference) algorithm and the modified PID(Proportional Integral Derivative)-based pan/tilt controller. That is, in the proposed system, the location coordinates of the target object in the right and left images are extracted from the sequential stereo input image by applying a region-based MAD algorithm and the configuration parameter of the stereo camera, and then these values could effectively control to pan/tilt of the stereo camera under the noisy circumstances through the modified PID controller. Accordingly, an adaptive control effect of a moving object can be analyzed through the advanced system with the proposed 3D robot vision, in which the possibility of real-time implementation of the robot vision system is also confirmed.

• The Journal of the Korea institute of electronic communication sciences
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• v.7 no.5
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• pp.1033-1039
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• 2012

In this paper, a simple and robust algorithm is proposed for detecting each steel plate from a image which contains several steel plates. Steel plate is characterized by line edge, so line detection is a fundamental task for analyzing and understanding of steel plate images. To detect the line edge, the proposed algorithm uses the small eigenvalue analysis. The proposed approach scans an input edge image from the top left corner to the bottom right corner with a moving mask. A covariance matrix of a set of edge pixels over a connected region within the mask is determined and then the statistical and geometrical properties of the small eigenvalue of the matrix are explored for the purpose of straight line detection. Using the detected line edges, each plate is determined based on the directional information and the distance information of the line edges. The results of the experiments emphasize that the proposed algorithm detects each steel plate from a image effectively.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.15 no.7
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• pp.1479-1486
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• 2011

In this paper, a simple and robust algorithm is proposed for detecting straight line segments in a steel plate image. Line detection from a steel plate image is a fundamental task for analyzing and understanding of the image. The proposed algorithm is based on small eigenvalue analysis. The proposed approach scans an input edge image from the top left comer to the bottom right comer with a moving mask. A covariance matrix of a set of edge pixels over a connected region within the mask is determined and then the statistical and geometrical properties of the small eigenvalue of the matrix are explored for the purpose of straight line detection. Before calculating the eigenvalue, each line segment is separated from the edge image where several line segments are overlapped to increase the accuracy of the line detection. Additionally, unnecessary line segments are eliminated by the number of pixels and the directional information of the detected line edges. The respects of the experiments emphasize that the proposed algorithm outperforms the existing algorithm which uses small eigenvalue analysis.

• The KIPS Transactions:PartB
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• v.19B no.2
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• pp.127-134
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• 2012

Video digital door lock(VDDL) system detects people who access to the door and acquires the human image. Design considerations is that current consumption must be minimized by applying fast human detection algorithm because of battery-based operation. Since the digital door lock takes an image through a fixed camera, detection of a person based on background image leads to high degree of reliability. This paper deals with a single human detection algorithm suitable for VDDL with fulfilling these requirements such that it detects a moving object in an image, then identifies whether the object is a person or not using image processing. The proposed image processing algorithm consists of two steps: Firstly, it detects the human image region using both background image and skin color information. Secondly, it identifies the person using polar histogram based on proportional information of human body. Proposed algorithm is implemented in VDDL and is verified the performance through experiments.

• The KIPS Transactions:PartB
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• v.14B no.4
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• pp.249-254
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• 2007

A mobile robot with various types of sensors and wireless camera is introduced. We show this mobile robot can detect objects well by combining the results of active sensors and image processing algorithm. First, to detect objects, active sensors such as infrared rays sensors and supersonic waves sensors are employed together and calculates the distance in real time between the object and the robot using sensor's output. The difference between the measured value and calculated value is less than 5%. We focus on how to detect a object region well using image processing algorithm because it gives robots the ability of working for human. This paper suggests effective visual detecting system for moving objects with specified color and motion information. The proposed method includes the object extraction and definition process which uses color transformation and AWUPC computation to decide the existence of moving object. Shape information and signature algorithm are used to segment the objects from background regardless of shape changes. We add weighing values to each results from sensors and the camera. Final results are combined to only one value which represents the probability of an object in the limited distance. Sensor fusion technique improves the detection rate at least 7% higher than the technique using individual sensor.