• Title/Summary/Keyword: Pose Detection

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Location Estimation and Obstacle tracking using Laser Scanner for Indoor Mobile Robots (실내형 이동로봇을 위한 레이저 스캐너를 이용한 위치 인식과 장애물 추적)

  • Choi, Bae-Hoon;Kim, Beom-Seong;Kim, Eun-Tai
    • Journal of the Korean Institute of Intelligent Systems
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    • v.21 no.3
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    • pp.329-334
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    • 2011
  • This paper presents the method for location estimation with obstacle tracking method. A laser scanner is used to implement the system, and we assume that the map information is known. We matches the measurement of the laser scanner to estimate the location of the robot by using sequential monte carlo (SMC) method. After estimating the robot's location, the pose of obstacles are detected and tracked, hence, we can predict the collision risk of them. Finally, we present the experiment results to verify the proposed method.

Real-time Multiple Pedestrians Tracking for Embedded Smart Visual Systems

  • Nguyen, Van Ngoc Nghia;Nguyen, Thanh Binh;Chung, Sun-Tae
    • Journal of Korea Multimedia Society
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    • v.22 no.2
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    • pp.167-177
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    • 2019
  • Even though so much progresses have been achieved in Multiple Object Tracking (MOT), most of reported MOT methods are not still satisfactory for commercial embedded products like Pan-Tilt-Zoom (PTZ) camera. In this paper, we propose a real-time multiple pedestrians tracking method for embedded environments. First, we design a new light weight convolutional neural network(CNN)-based pedestrian detector, which is constructed to detect even small size pedestrians, as well. For further saving of processing time, the designed detector is applied for every other frame, and Kalman filter is employed to predict pedestrians' positions in frames where the designed CNN-based detector is not applied. The pose orientation information is incorporated to enhance object association for tracking pedestrians without further computational cost. Through experiments on Nvidia's embedded computing board, Jetson TX2, it is verified that the designed pedestrian detector detects even small size pedestrians fast and well, compared to many state-of-the-art detectors, and that the proposed tracking method can track pedestrians in real-time and show accuracy performance comparably to performances of many state-of-the-art tracking methods, which do not target for operation in embedded systems.

Detection of Haemosporidia in Healthy Pet Parrots in South Korea

  • Hong, Sunghyun S.;Kim, Sungryong;Han, Jae-Ik;Na, Ki-Jeong
    • Journal of Veterinary Clinics
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    • v.38 no.1
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    • pp.1-6
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    • 2021
  • Avian haemosporidia, including malarial parasites, are geologically and biologically widespread. The protozoal pathogen has been a subject of intensive research in the past, which has resulted in major medical progress. Haemosporidia infection in avian species in South Korea has been studied in wild birds and layer flocks, but not in pet birds. At the Veterinary Teaching Hospital of Chungbuk National University, 75 birds that presented for health check-up were tested to evaluate the infection rate of Haemoproteus, Plasmodium and Leucocytozoon in birds without clinical symptoms. Haemoproteus spp. and Leucocytozoon spp. were simultaneously detected in a Major Mitchell's cockatoo (Lophochroa leadbeateri) by polymerase chain reaction, representing 1.33% of the tested birds. Phylogenetic analysis suggested that the infective Haemoproteus and Leucocytozoon strains were similar to those detected in foreign countries rather than those detected in the wild birds of Korea. Although the infection rate may not be indicative of a substantial infection in healthy pet parrots, the import of infected birds can pose a threat by allowing foreign pathogens to infect the local wild flocks or livestock. This is the first surveillance study of avian haemosporidia in pet parrots in South Korea.

An Evaluation Method for the Musculoskeletal Hazards in Wood Manufacturing Workers Using MediaPipe (MediaPipe를 이용한 목재 제조업 작업자의 근골격계 유해요인 평가 방법)

  • Jung, Sungoh;Kook, Joongjin
    • Journal of the Semiconductor & Display Technology
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    • v.21 no.2
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    • pp.117-122
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    • 2022
  • This paper proposes a method for evaluating the work of manufacturing workers using MediaPipe as a risk factor for musculoskeletal diseases. Recently, musculoskeletal disorders (MSDs) caused by repeated working attitudes in industrial sites have emerged as one of the biggest problems in the industrial health field while increasing public interest. The Korea Occupational Safety and Health Agency presents tools such as NIOSH Lifting Equations (NIOSH), OWAS (Ovako Working-posture Analysis System), Rapid Upper Limb Assessment (RULA), and Rapid Entertainment Assessment (REBA) as ways to quantitatively calculate the risk of musculoskeletal diseases that can occur due to workers' repeated working attitudes. To compensate for these shortcomings, the system proposed in this study obtains the position of the joint by estimating the posture of the worker using the posture estimation learning model of MediaPipe. The position of the joint is calculated using inverse kinetics to obtain an angle and substitute it into the REBA equation to calculate the load level of the working posture. The calculated result was compared to the expert's image-based REBA evaluation result, and if there was a result with a large error, feedback was conducted with the expert again.

BIM model-based structural damage localization using visual-inertial odometry

  • Junyeon Chung;Kiyoung Kim;Hoon Sohn
    • Smart Structures and Systems
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    • v.31 no.6
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    • pp.561-571
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    • 2023
  • Ensuring the safety of a structure necessitates that repairs are carried out based on accurate inspections and records of damage information. Traditional methods of recording damage rely on individual paper-based documents, making it challenging for inspectors to accurately record damage locations and track chronological changes. Recent research has suggested the adoption of building information modeling (BIM) to record detailed damage information; however, localizing damages on a BIM model can be time-consuming. To overcome this limitation, this study proposes a method to automatically localize damages on a BIM model in real-time, utilizing consecutive images and measurements from an inertial measurement unit in close proximity to damages. The proposed method employs a visual-inertial odometry algorithm to estimate the camera pose, detect damages, and compute the damage location in the coordinate of a prebuilt BIM model. The feasibility and effectiveness of the proposed method were validated through an experiment conducted on a campus building. Results revealed that the proposed method successfully localized damages on the BIM model in real-time, with a root mean square error of 6.6 cm.

Violence Detection System in Streaming Service and SNS Using Artificial Intelligence Technologies (인공지능을 활용한 스트리밍 서비스/SNS 내에서의 폭력 감지 시스템)

  • Kim, Seon-Min;Lee, Seok-Won;Lim, Seung-Su;Choi, Sangil
    • Proceedings of the Korea Information Processing Society Conference
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    • 2020.05a
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    • pp.442-445
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    • 2020
  • 인터넷 및 IT 기술의 발전과 더불어 미디어산업에도 큰 변화가 일어나고 있다. TV 를 대신하여 스트리밍 서비스를 이용하는 사람들이 늘고 있으며 SNS 를 활용하여 서로의 경험을 간접적으로 공유하는 형태의 새로운 문화 컨텐츠가 자리잡아가고 있다. 하지만 이러한 컨텐츠를 소비하는 주요 계층 중에는 초중고 학생들도 포함되어 있다. 인터넷 혹은 SNS 에서 소비되는 컨텐츠들을 관리 감독하는 컨트롤 타워가 부족하거나 전무하기 때문에 폭력, 음주, 흡연 등 사회적으로 악영향을 줄 수 있는 영상 또는 사진이 무분별하게 생산되어 청소년들에 의해 소비되고 있으며 더 나아가 이것이 사회적 문제로까지 대두되고 있다. 이러한 문제를 해결하기 위해 인공지능 기술을 활용한 여러 다양한 감시 시스템 개발을 위한 연구가 한창이다. 본 연구에서는 SNS 및 스트리밍 서비스에서 제공되는 영상 및 사진을 Pose Estimation 및 표정 인식 기술을 활용하여 폭력을 자동적으로 감지할 수 있는 폭력 감지 시스템을 개발하는데 그 목적이 있다.

Efficient Detection of Heavy Metal Lead Ions in Aqueous Media using Aggregation-Induced Emission (AIE)-based Turn-on Fluorescence Sensor (Aggregation-Induced Emission (AIE) 기반의 Turn-On 형광센서를 이용한 수질 속 중금속 납 이온의 효율적인 검출 )

  • Haemin Choi;Hyeonjeong Seong;Juyeon Cha;Seoung Ho Lee
    • Journal of Environmental Science International
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    • v.32 no.11
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    • pp.757-765
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    • 2023
  • Lead, a heavy metal widely employed in various industries, continues to pose a threat to both human health and the environment. Therefore, the development of a sensor capable of rapidly and accurately detecting lead(II) ions in real-time at contaminated sites is crucial. In this study, we have engineered a fluorescent sensor with the ability to efficiently detect lead(II) ions under actual environmental conditions, including tap water and freshwater. The compound, tetraphenylethylene carboxylic acid derivative (TPE-COOH), exhibits high selectivity and sensitivity toward lead(II) ions in aqueous solution, where the interaction between TPE-COOH and lead(II) ions leads to its aggregation, thus triggering a fluorescence "turn-on" based on the aggregation-induced emission (AIE) mechanism. Impressively, compound TPE-COOH proficiently detects lead(II) ions within a range of 30 to 100 𝜇M in tap water and freshwater, even in the presence of various interfering substances.

Estimating Interest Levels based on Visitor Behavior Recognition Towards a Guide Robot (안내 로봇을 향한 관람객의 행위 인식 기반 관심도 추정)

  • Ye Jun Lee;Juhyun Kim;Eui-Jung Jung;Min-Gyu Kim
    • The Journal of Korea Robotics Society
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    • v.18 no.4
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    • pp.463-471
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    • 2023
  • This paper proposes a method to estimate the level of interest shown by visitors towards a specific target, a guide robot, in spaces where a large number of visitors, such as exhibition halls and museums, can show interest in a specific subject. To accomplish this, we apply deep learning-based behavior recognition and object tracking techniques for multiple visitors, and based on this, we derive the behavior analysis and interest level of visitors. To implement this research, a personalized dataset tailored to the characteristics of exhibition hall and museum environments was created, and a deep learning model was constructed based on this. Four scenarios that visitors can exhibit were classified, and through this, prediction and experimental values were obtained, thus completing the validation for the interest estimation method proposed in this paper.

Enhancing Internet of Things Security with Random Forest-Based Anomaly Detection

  • Ahmed Al Shihimi;Muhammad R Ahmed;Thirein Myo;Badar Al Baroomi
    • International Journal of Computer Science & Network Security
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    • v.24 no.6
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    • pp.67-76
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    • 2024
  • The Internet of Things (IoT) has revolutionized communication and device operation, but it has also brought significant security challenges. IoT networks are structured into four levels: devices, networks, applications, and services, each with specific security considerations. Personal Area Networks (PANs), Local Area Networks (LANs), and Wide Area Networks (WANs) are the three types of IoT networks, each with unique security requirements. Communication protocols such as Wi-Fi and Bluetooth, commonly used in IoT networks, are susceptible to vulnerabilities and require additional security measures. Apart from physical security, authentication, encryption, software vulnerabilities, DoS attacks, data privacy, and supply chain security pose significant challenges. Ensuring the security of IoT devices and the data they exchange is crucial. This paper utilizes the Random Forest Algorithm from machine learning to detect anomalous data in IoT devices. The dataset consists of environmental data (temperature and humidity) collected from IoT sensors in Oman. The Random Forest Algorithm is implemented and trained using Python, and the accuracy and results of the model are discussed, demonstrating the effectiveness of Random Forest for detecting IoT device data anomalies.

Comparison of Artificial Intelligence Multitask Performance using Object Detection and Foreground Image (물체탐색과 전경영상을 이용한 인공지능 멀티태스크 성능 비교)

  • Jeong, Min Hyuk;Kim, Sang-Kyun;Lee, Jin Young;Choo, Hyon-Gon;Lee, HeeKyung;Cheong, Won-Sik
    • Journal of Broadcast Engineering
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    • v.27 no.3
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    • pp.308-317
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    • 2022
  • Researches are underway to efficiently reduce the size of video data transmitted and stored in the image analysis process using deep learning-based machine vision technology. MPEG (Moving Picture Expert Group) has newly established a standardization project called VCM (Video Coding for Machine) and is conducting research on video encoding for machines rather than video encoding for humans. We are researching a multitask that performs various tasks with one image input. The proposed pipeline does not perform all object detection of each task that should precede object detection, but precedes it only once and uses the result as an input for each task. In this paper, we propose a pipeline for efficient multitasking and perform comparative experiments on compression efficiency, execution time, and result accuracy of the input image to check the efficiency. As a result of the experiment, the capacity of the input image decreased by more than 97.5%, while the accuracy of the result decreased slightly, confirming the possibility of efficient multitasking.