• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1856-1869
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• 2015

This paper addresses the issues of 3D human activity detection, tracking and recognition from RGB-D video sequences using a feature structured framework. During human tracking and activity recognition, initially, dense depth images are captured using depth camera. In order to track human silhouettes, we considered spatial/temporal continuity, constraints of human motion information and compute centroids of each activity based on chain coding mechanism and centroids point extraction. In body skin joints features, we estimate human body skin color to identify human body parts (i.e., head, hands, and feet) likely to extract joint points information. These joints points are further processed as feature extraction process including distance position features and centroid distance features. Lastly, self-organized maps are used to recognize different activities. Experimental results demonstrate that the proposed method is reliable and efficient in recognizing human poses at different realistic scenes. The proposed system should be applicable to different consumer application systems such as healthcare system, video surveillance system and indoor monitoring systems which track and recognize different activities of multiple users.

• ETRI Journal
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• v.36 no.4
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• pp.662-672
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• 2014

Human action recognition is used in areas such as surveillance, entertainment, and healthcare. This paper proposes a system to recognize both single and continuous human actions from monocular video sequences, based on 3D human modeling and cyclic hidden Markov models (CHMMs). First, for each frame in a monocular video sequence, the 3D coordinates of joints belonging to a human object, through actions of multiple cycles, are extracted using 3D human modeling techniques. The 3D coordinates are then converted into a set of geometrical relational features (GRFs) for dimensionality reduction and discrimination increase. For further dimensionality reduction, k-means clustering is applied to the GRFs to generate clustered feature vectors. These vectors are used to train CHMMs separately for different types of actions, based on the Baum-Welch re-estimation algorithm. For recognition of continuous actions that are concatenated from several distinct types of actions, a designed graphical model is used to systematically concatenate different separately trained CHMMs. The experimental results show the effective performance of our proposed system in both single and continuous action recognition problems.

• Journal of Korea Multimedia Society
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• v.17 no.5
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• pp.556-565
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• 2014

This paper proposes a method to track object of real-time video transferred through single web-camera and to recognize risk-situation and human actions. The proposed method recognizes human basic actions that human can do in daily life and finds risk-situation such as faint and falling down to classify usual action and risk-situation. The proposed method models the background, obtains the difference image between input image and the modeled background image, extracts human object from input image, tracts object's motion and recognizes human actions. Tracking object uses the moment information of extracting object and the characteristic of object's recognition is moment's change and ratio of object's size between frames. Actions classified are four actions of walking, waling diagonally, sitting down, standing up among the most actions human do in daily life and suddenly falling down is classified into risk-situation. To test the proposed method, we applied it for eight participants from a video of a web-cam, classify human action and recognize risk-situation. The test result showed more than 97 percent recognition rate for each action and 100 percent recognition rate for risk-situation by the proposed method.

• Journal of the Semiconductor & Display Technology
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• v.21 no.4
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• pp.92-98
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• 2022

This paper proposes a new method of the traffic signal detection and the recognition in an HSI color model. The proposed method firstly converts a ROI image in the RGB model to in the HSI model to segment the color of a traffic signal. Secondly, the segmented colors are dilated by the morphological processing to connect the traffic signal light and the signal light case and finally, it extracts the traffic signal light and the case by the aspect ratio using the connected component analysis. The extracted components show the detection and the recognition of the traffic signal lights. The proposed method is implemented using C language in Raspberry Pi 4 system with a camera module for a real-time image processing. The system was fixedly installed in a moving vehicle, and it recorded a video like a vehicle black box. Each frame of the recorded video was extracted, and then the proposed method was tested. The results show that the proposed method is successful for the detection and the recognition of traffic signals.

• Journal of Information Processing Systems
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• v.19 no.6
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• pp.730-744
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• 2023

The topic of this paper is the recognition of human activities using egocentric vision, particularly captured by body-worn cameras, which could be helpful for video surveillance, automatic search and video indexing. This being the case, it could also be helpful in assistance to elderly and frail persons for revolutionizing and improving their lives. The process throws up the task of human activities recognition remaining problematic, because of the important variations, where it is realized through the use of an external device, similar to a robot, as a personal assistant. The inferred information is used both online to assist the person, and offline to support the personal assistant. With our proposed method being robust against the various factors of variability problem in action executions, the major purpose of this paper is to perform an efficient and simple recognition method from egocentric camera data only using convolutional neural network and deep learning. In terms of accuracy improvement, simulation results outperform the current state of the art by a significant margin of 61% when using egocentric camera data only, more than 44% when using egocentric camera and several stationary cameras data and more than 12% when using both inertial measurement unit (IMU) and egocentric camera data.

• Journal of the Korea Society of Computer and Information
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• v.15 no.6
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• pp.29-35
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• 2010

While diversification of the use of video in the prevalence of cheap video equipment, augmented reality can print additional real-world images and video image. Although many recent advent augmented reality techniques, currently attempting to correct the character recognition is performed. In this paper characters marked with a visual marker recognition, and the color to match the marker color of the characters finds. And, it was shown on the screen by the character recognition. In this paper, by applying the phoneme pattern segmentation algorithm by the horizontal projection, we propose to segment the phoneme to match the six types of Hangul representation. Throughout the experiment sample of phoneme segmentation using augmented reality showed proceeding result at each step, and the experimental results was found to be that detection rate was above 90%.

• Journal of the Korea Society of Computer and Information
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• v.16 no.10
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• pp.53-62
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• 2011

In this paper, high-tech IT Convergence, to the development of complex technology, special technology, video object recognition technology was considered only as a smart - phone technology with the development of personal portable terminal has been developed crossroads. Technology-based detection of 3D face recognition technology that recognizes objects detected through the intelligent video recognition technology has been evolving technologies based on image recognition, face detection technology with through the development speed is booming. In this paper, based on human face recognition technology to detect the object recognition image processing technology is applied through the face recognition technology applied to the IP camera is the party of the mouth, and allowed the ability to identify and apply the human face recognition, measurement techniques applied research is suggested. Study plan: 1) face model based face tracking technology was developed and applied 2) algorithm developed by PC-based measurement of human perception through the CPU load in the face value of their basic parameters can be tracked, and 3) bilateral distance and the angle of gaze can be tracked in real time, proved effective.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.11a
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• pp.675-680
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• 2008

This paper proposes a way to measure the displacement of a multi-point by using a pattern recognition from video signal. Generally in measuring displacement, gab sensor, which is a displacement sensor, is used. However, it is difficult to measure displacement by using a common sensor in places where it is unsuitable to attach a sensor, such as high-temperature areas or radioactive places. In this kind of places, non-contact methods should be used to measure displacement and in this study, images of CCD camera were used. When displacement is measure by using camera images, it is possible to measure displacement with a non-contact method. It is simple to install and multi-point displacement measuring device so that it is advantageous to solve problems of spatial constraints.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.573-574
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• 1992

We developed a recognition and tracking system of moving objects. The system consists of one CCD video camera, two DC motors in horizontal and vertical axles with encoders, pluse width modulation(PWM) driving unit, 16 bit NEC 9801 microcomputer, and their interfaces. The recognition and tracking system is able to recognize shape and size of a moving object and is able to track the object within a certain range of errors. This paper presents the brief introduction of the recognition and tracking system developed in our laboratory.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.9
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• pp.139-145
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• 2018

It is the first step to survive cardiac arrest for the general public to recognize cardiac arrest rapidly. The purpose of this study is to investigate the effective method of education by analyzing the degree of cardiac arrest recognition after performing the agonal breathing education in a video lecture or instructor-led lecture. The study subjects were assigned to either a video lecture or instructor-led lecture in a randomized way and were compared after the education according to the degree of recognition of cardiac arrest in the randomized controlled study. The study was conducted from October 30, 2015 to October 31, 2015. And the study subjects were 104 youths aged 15 years or older, of which 52 were selected as the experimental group and the remaining 52 as the control group. The results did not show a significant difference between these two groups when the subjects are given the video lecture where patients showed no reaction or sign of breathing(p=0.741). However, in the video lecture where there was no reaction of patients but still sign of agonal breathing, 43 people(82.7%) in the experimental group and 33 people(63.5%) in control group have successfully performed CPR and there has been a significant difference (p=0.006). Therefore, we could conclude that video lecture was more efficient than instructor-led lecture when teaching CPR.