• ETRI Journal
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• v.42 no.2
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• pp.230-238
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• 2020

Human-object interaction (HOI) detection is a popular computer vision task that detects interactions between humans and objects. This task can be useful in many applications that require a deeper understanding of semantic scenes. Current HOI detection networks typically consist of a feature extractor followed by detection layers comprising small filters (eg, 1 × 1 or 3 × 3). Although small filters can capture local spatial features with a few parameters, they fail to capture larger context information relevant for recognizing interactions between humans and distant objects owing to their small receptive regions. Hence, we herein propose a three-stream HOI detection network that employs a context convolution module (CCM) in each stream branch. The CCM can capture larger contexts from input feature maps by adopting combinations of large separable convolution layers and residual-based convolution layers without increasing the number of parameters by using fewer large separable filters. We evaluate our HOI detection method using two benchmark datasets, V-COCO and HICO-DET, and demonstrate its state-of-the-art performance.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.184-184
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• 2000

The new-type measuring modules for unmanned remote supervisory system using mobile communication network have been designed in this study. Measuring modules consist of temperature measuring module, humidity measuring module and human body sensing module. And we will design a main part to collect and process informations of each modules, evaluate reliability of combined total system.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.10
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• pp.223-227
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• 2014

This paper presents an improvement method for human body sensing module and management system, specifically focused on the human body detection module with ultrasonic sensors to detect the usage of toilets and the management system to control the state of the toilets of the entire building. The proposed human body sensing module consists of the detection sensor to detect the movement of human body and the contact sensor to detect the position in a certain distance. The management system is configured of the control unit to process the signal transmitted from sensors, opening and closing valves according to the sensing signal, and the short range wireless communication unit to save the operational status data as well as transmit the data at regular intervals.

• Journal of Sensor Science and Technology
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• v.23 no.3
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• pp.149-157
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• 2014

This paper considers the ability of counting and positioning multi-targets by using a mobile UWB radar device. After a background subtraction process, distinguishing between clutters and human body signals, the position of targets will be computed using weighted Gaussian mixture methods. While computer vision offers many advantages, it has limited performance in poor visibility conditions (e.g., at night, haze, fog or smoke). UWB radar can provide a complementary technology for detecting and tracking humans, particularly in poor visibility or through-wall conditions. As we know, for 2D measurement, one method is the use of at least two receiver antennas. Another method is the use of one mobile radar receiver. This paper tried to investigate the position detection of the stationary human body using the movement of one UWB radar module.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2000.10a
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• pp.534-538
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• 2000

The new-type measuring modules for unmanned remote supervisory system using mobile communication network have been designed in this study. Measuring modules consist of temperature measuring module, humidity measuring module and human body sensing module. And we will design a main part to collect and process informations of each modules, evaluate reliability of combined total system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.6
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• pp.811-817
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• 2013

In this study we present a system for pulse-rate period detection and human sensibility evaluation based on the wireless medical telemetry service (WMTS) used for transmission of data from medical telemetry devices to various medical facilities and services. We develop a medical-purpose specific WMTS communication module to transmit biometric signals. From the pulse rate variability(PRV) signal, we attempt to classify positive and negative emotional states based on analysis of the ratio of LF/HF in the frequency domain. We measure the data reception rate according to distance in order to test the performance of the WMTS module and analyze the effects on human sensibility evaluation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.12
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• pp.427-436
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• 2016

Due to disasters such as earthquakes and landslides in urban areas, persons have been buried inside collapsed buildings and structures. Rescuers have mainly utilized detection equipment by applying sound, video and electric waves, but these are expensive and due to the directional approaches onto the collapsed site, secondary collapse risk can arise. In addition, due to poor utilization of such equipment, new human detection technology with quick and high reliability has not been utilized. To address these issues, this study develops a wireless signal-based human detection module that can be loaded into an Unmanned Aerial Vehicle (UAV). The human detection module searches for the 3D location for buried persons by collecting Wi-Fi signal and barometer sensors data transmitted from the mobile phones. This module can gain diverse information from mobile phones for buried persons in real time. We present a development framework of the module that provides 3D location data with more reliable information by delivering the collected data into a local computer in the ground. This study verified the application feasibility of the developed module in a real collapsed area. Therefore, it is expected that these results can be used as a core technology for the quick detection of buried persons' location and for relieving them after disasters that induce building collapses.

• Journal of electromagnetic engineering and science
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• v.12 no.4
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• pp.234-239
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• 2012

This paper presents a compact K-band Doppler radar sensor for human vital signal detection that uses a radar configuration with only single coupler. The proposed radar front-end configuration can reduce the chip size and the additional RF power loss. The radar front-end IC is composed of a Lange coupler, VCO, and single balanced mixer. The oscillation frequency of the VCO is from 27.3 to 27.8 GHz. The phase noise of the VCO is -91.2 dBc/Hz at a 1 MHz offset frequency, and the output power is -4.8 dBm. The conversion gain of the mixer is about 11 dB. The chip size is $0.89{\times}1.47mm^2$. The compact Ka-band Doppler radar system was developed in order to demonstrate remote human vital signal detection. The radar system consists of a Ka-band Doppler radar module with a $2{\times}2$ patch array antenna, baseband signal conditioning block, DAQ system, and signal processing program. The front-end module size is $2.5{\times}2.5cm^2$. The proposed radar sensor can properly capture a human heartbeat and respiration rate at the distance of 50 cm.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.3B
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• pp.311-317
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• 2009

The traditional network anomaly detection systems execute the threshold-based detection without considering dynamic network environments, which causes false positive and limits an effective resource utilization. To overcome the drawbacks, we present the adaptive network anomaly detection model based on artificial immune system (AIS) in centralized network. AIS is inspired from human immune system that has learning, adaptation and memory. In our proposed model, the interaction between dendritic cell and T-cell of human immune system is adopted. We design the main components, such as central node and router node, and define functions of them. The central node analyzes the anomaly information received from the related router nodes, decides response policy and sends the policy to corresponding nodes. The router node consists of detector module and responder module. The detector module perceives the anomaly depending on learning data and the responder module settles the anomaly according to the policy received from central node. Finally we evaluate the possibility of the proposed detection model through simulation.

• IEMEK Journal of Embedded Systems and Applications
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• v.8 no.3
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• pp.137-143
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• 2013

This paper describes an implementation of face detection technology for a real-time application on the Android platform. Java class of Face-Detection for detection of human face is provided by the Android API. However, this function is not suitable to apply for the real-time applications due to inadequate detection speed and accuracy. In this paper, the AdaBoost based classification method which utilizes Local Binary Pattern (LBP) histogram is employed for face detection. The face detection module has been developed by C/C++ language for high-speed image processing, and this module is included to the Android platform using the Java Native Interface (JNI). The experiments were carried out in the Java-based environment and JNI-based environment. The experimental results have shown that the performance of JNI-based is faster than Java-based method and our system is well enough to apply for real-time applications.