• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.10
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• pp.1095-1102
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• 2014

In this paper, We proposed the approach of GPU and motion vector to analysis the Human activity in real-time surveillance system. The most important part, that is detect blob(human) in the foreground. We use to detect Adaptive Gaussian Mixture, Weighted subtraction image for salient motion and motion vector. And then, We use motion vector for human activity analysis. In this paper, the activities of human recognize and classified such as meta-classes like this {Active, Inactive}, {Position Moving, Fixed Moving}, {Walking, Running}. We created approximately 300 conditions for the simulation. As a result, We showed a high success rate about 86~98%. The results also showed that the high resolution experiment by the proposed GPU-based method was over 10 times faster than the cpu-based method.

• International Journal of Computer Science & Network Security
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• v.21 no.4
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• pp.131-139
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• 2021

In current time, anomaly detection is the primary concern of the administrative authorities. Suspicious activity identification is shifting from a human operator to a machine-assisted monitoring in order to assist the human operator and react to an unexpected incident quickly. These automatic surveillance systems face many challenges due to the intrinsic complex characteristics of video sequences and foreground human motion patterns. In this paper, we propose a novel approach to detect anomalous human activity using a hybrid approach of statistical model and Genetic Programming. The feature-set of local motion patterns is generated by a statistical model from the video data in an unsupervised way. This features set is inserted to an enhanced Genetic Programming based classifier to classify normal and abnormal patterns. The experiments are performed using publicly available benchmark datasets under different real-life scenarios. Results show that the proposed methodology is capable to detect and locate the anomalous activity in the real time. The accuracy of the proposed scheme exceeds those of the existing state of the art in term of anomalous activity detection.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.59.4-59
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• 2001

In this paper, wireless interface of the motion between human and robot is implemented. The idea is that if a human who is equiped with device including accelerometer and rate-gyro sensor move his/her arm, then the robot follows human motion. The robot is designed as wheeled type mobile robot with two link arms. The robot´s basic movements such as forward, backward, left, right movement can be controlled from foot sensor which human steps on. Arm movements can be controlled by arm motion of human motion. In order to detect human motion, sensor data analysis from gyro and accelerometer has to be done. Data from sensors are transferred through wireless communication to activate the robot.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.3 no.1
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• pp.84-95
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• 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 Sensor Science and Technology
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• v.29 no.5
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• pp.312-322
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• 2020

As the use of mobile devices increase, there is public interest in the utilization of the human motion generated mechanical energy. The human motion generated mechanical energies vary depending on the body region, type of motion, etc., and an appropriate device has to be designed to utilize them effectively. In this work, a device based on the principles of triboelectric generation and inertia was assessed in order to utilize the multi-axial mechanical energies generated by human motions. To improve the output performance we confirm the changes in the output that vary with the structural design, the reasons for such changes, and variations in performance based on the parts of the human body. In addition, the level of electrical energy generated based on motion type was measured; a maximum voltage of 30 V and a current of 2 ㎂ were generated. Finally, the proposed device was utilized in LEDs used for lighting, thus demonstrating that multi-axial mechanical energies can be harvested effectively. Based on the results, we expect that the developed device can be utilized as a sensor to detect mechanical energies, to sense changes in motion, or as a generator for auxiliary power supply for mobile devices.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.8
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• pp.870-877
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• 2013

There has been much recent research interest in developing numerous kinds of human-machine interface. This field currently requires more accurate and reliable sensing systems to detect the intended human motion. Most conventional human-machine interface use electromyography (EMG) sensors to detect the intended motion. However, EMG sensors have a number of disadvantages and, as a consequence, the human-machine interface is difficult to use. This study describes a muscle volume sensor (MVS) that has been developed to measure variation in the outline of a muscle, for use as a human-machine interface. We developed an algorithm to calibrate the system, and the feasibility of using MVS for detecting muscular activity was demonstrated experimentally. We evaluated the performance of the MVS via isotonic contraction using the KIN-COM$^{(R)}$ equipment at torques of 5, 10, and 15 Nm.

• Journal of Korea Multimedia Society
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• v.10 no.6
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• pp.716-725
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• 2007

In this paper we describe a motion tracking algorithm for 3D human animation using stereo vision system. This allows us to extract the motion data of the end effectors of human body by following the movement through segmentation process in HIS or RGB color model, and then blob analysis is used to detect robust shape. When two hands or two foots are crossed at any position and become disjointed, an adaptive algorithm is presented to recognize whether it is left or right one. And the real motion is the 3-D coordinate motion. A mono image data is a data of 2D coordinate. This data doesn't acquire distance from a camera. By stereo vision like human vision, we can acquire a data of 3D motion such as left, right motion from bottom and distance of objects from camera. This requests a depth value including x axis and y axis coordinate in mono image for transforming 3D coordinate. This depth value(z axis) is calculated by disparity of stereo vision by using only end-effectors of images. The position of the inner joints is calculated and 3D character can be visualized using inverse kinematics.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.625-626
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• 2013

This paper introduces a new method that detects human motions using a Kinect sensor. Also this paper describes a method to mimic the detected human motions. We first build a human stick model by processing the output of Kinect sensor. We detect a specific motion by using the position of each joint of the human stick model and by using the angles between joints.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.9
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• pp.1197-1201
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• 2018

In this paper, a Doppler radar for a localization of a human motion is demonstrated. In the system, we used a $8{\times}8$ phased array antenna using metamaterial phase shifters for easy and precise control of antenna beam pattern. Scanning area is a circular sector with an inscribed angle of $60^{\circ}$ and a diameter of 45m. This area is divided with 15 designated area and each area is scanned for 0.2 second. When there is a motion in a designated area, we are able to detect a frequency shift due to a Doppler effect. In this way it is possible to detect the location of motion. The experiment shows that 78% of position accuracy. The remaining 22% occurred the surroundings of the designated area.