• Journal of Navigation and Port Research
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• v.38 no.6
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• pp.645-653
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• 2014

A Bridge Navigational Watch Alarm System (hereafter 'BNWAS') is to monitor and detect if an officer of watch(hereafter 'OOW') keeps a sharp lookout on the bridge. The careless lookout of an OOW could lead to marine accidents. For this reason on June 5th, 2009, IMO decided that a ship is equipped with a BNWAS. However, an existing BNWAS gives the OOW a lot of inconvenience and stress in its operation. It requires that the OOW should press reset buttons to confirm their alert watch on the bridge at every three to twelve minute. Many OOWs have complained that at some circumstances they cannot focus on their bridge activities including watch-keeping due to a lots of resetting inputs of BNWAS. Accordingly, IMO has allowed the use of a motion sensor as a resetting device. The motion sensor detects the movements of human body on the bridge and subsequently sends reset signals directly to BNWAS automatically. As a result, OOWs can work uninterrupted. However, some of classification societies and flag authorities have a slightly different stance on the use of motion sensor as a resetting method for BNWAS. The reason is that the motion sensor may trigger false reset signals caused by the motion of objects on the bridge, especially a slight movement such as toss and turn of human body which can extend the period of careless watch. As a basic study to minimize the false reset signals, this paper proposes a simple configuration of BNWAS, which consists of only three motion sensors associated with 'AND' and 'OR' logic gates. Additionally, several considerations are also proposed for the implementation of motion sensors. This study found that the proposed configuration which consists of three motion sensors is better than an existing one by reducing false reset signals caused by a slight movement of human body in one's sleep. The proposed configuration in this paper filters false reset signals and is simple to be implemented on existing vessels. In addition, it can be easily installed just by a basic electrical knowledge.

• Journal of Broadcast Engineering
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• v.20 no.4
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• pp.557-567
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• 2015

Over the past decades, a variety of spatial saliency methods have been introduced. Recently, motion saliency has gained much interests, where motion data estimated from an image sequence are utilized. In general, motion saliency requires reliable motion data as well as image segmentation for producing satisfactory saliency map which poses difficulty in most natural images. To overcome this, we propose a motion-based saliency generation that enhances the spatial saliency based on the combination of spatial and motion saliencies as well as motion complexity without the consideration of complex motion classification and image segmentation. Further, an affinity model is integrated for the purpose of connecting close-by pixels with different colors and obtaining a similar saliency. In experiment, we performed the proposed method on eleven test sets. From the objective performance evaluation, we validated that the proposed method produces better result than spatial saliency based on objective evaluation as well as ROC test.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.6
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• pp.547-552
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• 2015

In this paper, an ARS-EKF based motion counting algorithm for repetitive exercises such as calisthenics is proposed using a smartwatch. Raw sensor signals from accelerometers and gyroscopes are widely used for conventional smartwatch counting algorithms based on pattern recognition. However, generated features from raw data are not intuitive to reflect the movement of motions. The proposed motion counter algorithm is composed of navigation based feature generation and counting with error correction. The candidate features for each activity are velocity and attitude calculated through an ARS-EKF algorithm. In order to select those features which reveal the characteristics of each motion, an exercise frame from the initial sensor frame is introduced. Counting processes are basically based on the zero crossing method, and misdetected counts are eliminated via simple classification algorithms considering the frequency of the counted motions. Experimental results show that the proposed algorithm efficiently and accurately counts the number of exercises.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.5
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• pp.327-330
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• 2016

Currently, the world's elderly population continues to grow at a dramatic rate. As the number of senior citizens increases, detection of someone falling has attracted increasing attention for visual surveillance systems. This paper presents a novel fall-detection algorithm using motion estimation and an integrated spatiotemporal energy map of the object region. The proposed method first extracts a human region using a background subtraction method. Next, we applied an optical flow algorithm to estimate motion vectors, and an energy map is generated by accumulating the detected human region for a certain period of time. We can then detect a fall using k-nearest neighbor (kNN) classification with the previously estimated motion information and energy map. The experimental results show that the proposed algorithm can effectively detect someone falling in any direction, including at an angle parallel to the camera's optical axis.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.2C
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• pp.78-85
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• 2011

In this paper we proposed an intelligent PTZ camera system which detects, classifies and tracks moving objects. If a moving object is detected, features are extracted for classification and then realtime tracking follows. We used GMM for detection followed by shadow removal. Legendre moment is used for classification. Without auto focusing, we can control the PTZ camera movement by using center points of the image and object's direction, distance and velocity. To implement the realtime system, we used TI DM6446 Davinci processor. Throughout the experiment, we obtained system's high performance in classification and tracking both at vehicle's normal and high speed motion.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.11a
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• pp.143-146
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• 2011

This paper presents human perception-based video coding method using an online learning framework. In this work, we analyze the relationship between human attention regions and video quality, and also consider human memory. We classify the motion patterns based on the analysis. Then, we devise a motion pattern classification method using Hedge algorithm. Along with the motion patterns, we smooth out the specific regions or sharpen details of the regions using the regional priorities. The preprocessed sequences are applied to the video codec. The performance is excellent on the overall quality as well as the regional quality.

• Journal of the Institute of Convergence Signal Processing
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• v.5 no.4
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• pp.287-293
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• 2004

This paper proposes an efficient motion compensation algorithm for video sequences with brightness variations. In the proposed algorithm, the brightness variation parameters are estimated and local motions are compensated. To detect the frame with large brightness variations, we employ the frame classification based on the cross entropy between histograms of two successive frames, which can reduce the computational redundancy. Simulation results show that the proposed method yields a higher peak signal to noise ratio (PSNR) than that of the conventional methods, with a low computational load, when the video scene contains large brightness changes.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.33 no.10
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• pp.381-386
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• 1984

This paper deals with the microcomputer realization of EMG pattern recognition system which provides identification of motion commands from the EMG signals for the on-line control of a prosthetic arm. A probabilistic model of pattern is formulated in the feature space of integral absolute value(IAV) to describe the relation between a motion command and the location of corresponding pattern. This model enables the derivation of sample density function of a command in the feature space of IAV. Classification is caried out through the multiclass sequential decision process, where the decision rule and the stopping rule of the process are designed by using the simple mathematical formulas defined as the likelihood probability and the decision measure, respectively. Some floating point algorithms such as addition, multiplication, division, square root and exponential function are developed for calculating the probability density functions and the decision measure. Only six primitive motions and one no motion are incorporated in this paper.

• Smart Media Journal
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• v.8 no.3
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• pp.47-52
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• 2019

In this paper, we designed HW / SW interfaces for processing the signals of capacitive sensors like Electric Potential Sensor (EPS) to detect the surrounding electric field disturbance as feature signals in motion recognition systems. We implemented a smart light control system with those interfaces. In the system, the on/off switch and brightness adjustment are controlled by hand gestures using the designed and fabricated interface circuits. PWM (Pulse Width Modulation) signals of the controller with a driver IC are used to drive the LED and to control the brightness and on/off operation. Using the hand-gesture signals obtained through EPS sensors and the interface HW/SW, we can not only construct a gesture instructing system but also accomplish the faster recognition speed by developing dedicated interface hardware including control circuitry. Finally, using the proposed hand-gesture recognition and signal processing methods, the light control module was also designed and implemented. The experimental result shows that the smart light control system can control the LED module properly by accurate motion detection and gesture classification.

• ETRI Journal
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• v.46 no.2
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• pp.250-262
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• 2024

Air-writing recognition is relevant in areas such as natural human-computer interaction, augmented reality, and virtual reality. A trajectory is the most natural way to represent air writing. We analyze the recognition accuracy of words written in air considering five features, namely, writing direction, curvature, trajectory, orthocenter, and ellipsoid, as well as different parameters of a hidden Markov model classifier. Experiments were performed on two representative datasets, whose sample trajectories were collected using a Leap Motion Controller from a fingertip performing air writing. Dataset D₁ contains 840 English words from 21 classes, and dataset D₂ contains 1600 English words from 40 classes. A genetic algorithm was combined with a hidden Markov model classifier to obtain the best subset of features. Combination ftrajectory, orthocenter, writing direction, curvatureg provided the best feature set, achieving recognition accuracies on datasets D₁ and D₂ of 98.81% and 83.58%, respectively.