• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.3 no.1
• /
• pp.84-95
• /
• 2009

The ubiquitous smart home is the home of the future, which exploits context information from both the human and the home environment, providing an automatic home service for the human. Human location and motion are the most important contexts in the ubiquitous smart home. In this paper, we present a real-time human tracker that predicts human location and motion for the ubiquitous smart home. The system uses four network cameras for real-time human tracking. This paper explains the architecture of the real-time human tracker, and proposes an algorithm for predicting human location and motion. To detect human location, three kinds of images are used: $IMAGE_1$ - empty room image, $IMAGE_2$ - image of furniture and home appliances, $IMAGE_3$ - image of $IMAGE_2$ and the human. The real-time human tracker decides which specific furniture or home appliance the human is associated with, via analysis of three images, and predicts human motion using a support vector machine (SVM). The performance experiment of the human's location, which uses three images, lasted an average of 0.037 seconds. The SVM feature of human motion recognition is decided from the pixel number by the array line of the moving object. We evaluated each motion 1,000 times. The average accuracy of all types of motion was 86.5%.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.34 no.5
• /
• pp.81-87
• /
• 2006

In this paper, an experimental optical head tracker system is designed and constructed. The system is composed of the infrared LEDs and two infrared CCD cameras to filter out the interference of another light in the limited environment like the cockpit. Then the optical head tracker algorithm is designed by using the feature detection algorithm and the 3D motion estimation algorithm. The feature detection algorithm, used to obtain the 2D position coordinates of the features on the image plane, is implemented by using the thresholding and the masking techniques. The 3D motion estimation algorithm which estimates the motion of a pilot's head is implemented by using the extended Kalman filter (EKF). Also, we used the precise rate table to verify the performance of the experimental optical head tracker system and compared the rotational performance of this system with the inertial sensor.

• Proceedings of the KIEE Conference
• /
• 2009.04a
• /
• pp.40-42
• /
• 2009

The economic growth and highly industrialized society have increased the demand for electricity power. As a result, concerns were focused on the energy resource scarcity and global warming. That is why the photovoltaic generation system to address these concerns has been in the spotlight recently. In this thesis, a utility interactive photovoltaic generation system was operated experimentally for the purpose of promoting the spread of the photovoltaic generation system in the future. Also, the effect of the type of array structure has on the performance of the photovoltaic generation system was evaluated quantitatively and by analyzing the comprehensive operating characteristics, the following results were obtained. In the demo system operated for a year, the average irradiation was measured to be 455,076 $[W/m^2]$ and the maximum irradiation to be 626,622 $[W/m^2]$ in May, up 171,546 $[W/m^2]$ or 38[%] compared with the average irradiation. The minimum irradiation was observed to be 294,022$[W/m^2]$ in December, down 161,054 $[W/m^2]$ or 35[%] compared with the average irradiation. The generation power in situation where there is plenty of irradiation was more than the average one, and the generation power in the fixed system amounted to 32[%], the single-axis tracker to 37[%], and the dual-axis tracker to 39[%]. The generation power in situation where there is little irradiation was less than the average one, and the generation power in the dual-axis tracker amounted to 41[%], the single-axis tracker to 40[%], and the fixed system to 36[%].

• Journal of the Institute of Electronics and Information Engineers
• /
• v.53 no.4
• /
• pp.129-135
• /
• 2016

In this paper, we propose a novel tracking algorithm called slide window tracker (SWT) suitable for maneuvering target. To efficiently estimate trajectory of moving target, we adopt a sliding piecewise linear window which includes past trace information. By adjusting the window parameters, the proposed algorithm is to reduce measurement noise and to track fast maneuvering target with little computational increment as compared to ${\alpha}-{\beta}$ tracker. Throughout the computer simulations, we verify outstanding tracking performance of the SWT algorithm in noisy linear and nonlinear trajectories. Also, we show that the SWT algorithm is not sensitive to initial model parameter selection, which gives large degree of freedom in applying the SWT algorithm to unknown time-varying measurement environments.

• Journal of Digital Contents Society
• /
• v.19 no.9
• /
• pp.1717-1726
• /
• 2018

This study is for looking into Factors affecting the Intention of using about Fitness Tracker based on IoT by Flow Theory and UTAUT Model. The independent variables of the IoT-based fitness tracker have been selected as Challenge, Skill Factors used in the Flow experience theory and selected the personal characteristics of health perception, enjoyment, Social Influence, and Facilitating Conditions. In addition UTAUT theory was used as parameter of Performance Expectancy and Effort Expectancy and intention of Use behavior was selected as a dependent variable. For verifying research hypothesis, This study collected 126 surveys data and carried out a statistical analysis by Smart PLS. The result shows that new Flow Experience and Personal Characteristic Factors with key variables in the UTAUT Model affect significantly to Intention of using about Fitness Wearable environment based IoT.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.45 no.10
• /
• pp.66-74
• /
• 2008

Surveillance system using active tracking camera has no distance limitation of surveillance range compared to supersonic or sound sensors. However, complex motion tracking algorithm requires huge amount of computation. Compared to conventional methods using DSPs or embedded processors, this paper proposes and implements a novel motion tracker unit that detects and extracts motion information of moving objects by using picture difference of consecutive frames. The proposed motion tracker unit was implemented in FPGA with about 13,000 gates. It processes NTSC format video and was verified by embedding it into the active surveillance camera system. We also propose and implements a motion estimator unit linkable to video codec by embedding the proposed motion tracker unit into ready-made motion estimator unit. The implemented motion estimator unit is about 17,000 gates in $0.35{\mu}m$ process.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.36 no.7
• /
• pp.680-687
• /
• 2008

This paper presents map creation algorithm and initial attitude estimation method for the proposed optical head tracker system. The optical head tracker system consists of the IR stereo cameras and infrared LEDs as features on the helmet. In order for the stereo camera to track the luminous LEDs, it must take in to account the light radiation from the LEDs to determine the position of the center points. The proposed map creation algorithm makes map data about the position of features center points on the helmet frame. Also, initial attitude estimation method is proposed to estimate the initial attitude and position of a pilot head from the camera frame by the use of the feature pattern on the helmet. Therefore, the head motion can be expressed with respect to the body frame of a flight.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.34 no.7
• /
• pp.63-70
• /
• 2006

This paper describes research of using area of artificial mark on vision based head tracker system. A head tracker system consists of the translational and rotational motions which are detected by web camera. Results of the motion are taken from image processing and neural network. Because of the characteristics of cockpit, the specific color on the helmet is tracked for translational motion. And rotational motion is tracked via neural network. Ratio of two different colored area on the helmet is used as input of network. Neural network algorithms used, such as back-propagation and RBFN (Radial Basis Function Network). Both back-propagation using a characteristic of feedback and RBFN using a characteristic of statistics have a good performances for the tracking of nonlinear system such as a head motion. Finally, this paper analyzes and compares with tracking performance.

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

• Atmosphere
• /
• v.22 no.4
• /
• pp.415-427
• /
• 2012

$CO_2$ is the most important trace gas related to climate change. Therefore, understanding surface carbon sources and sinks is important when seeking to estimate the impact of $CO_2$ on the environment and climate. CarbonTracker, developed by NOAA, is an inverse modeling system that estimates surface carbon fluxes using an ensemble Kalman filter with atmospheric $CO_2$ measurements as a constraint. In this study, to investigate the capability of CarbonTracker as an analysis tool for estimating surface carbon fluxes in Asia, an experiment with a nesting domain centered in Asia is performed. In general, the results show that setting a nesting domain centered in Asia region enables detailed estimations of surface carbon fluxes in Asia. From a rank histogram, the prior ensemble spread verified at observational sites located in Asia is well represented with a relatively flat rank histogram. The posterior flux in the Eurasian Boreal and Eurasian Temperate regions is well analyzed with proper seasonal cycles and amplitudes. On the other hand, in tropical regions of Asia, the posterior flux does not differ greatly from the prior flux due to fewer $CO_2$ observations. The root mean square error of the model $CO_2$ calculated by the posterior flux is less than the model $CO_2$ calculated by the prior flux, implying that CarbonTracker based on the ensemble Kalman filter works appropriately for the Asia region.