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계층적 군집화 기반 Re-ID를 활용한 객체별 행동 및 표정 검출용 영상 분석 시스템
이상현(Sang-Hyun Lee),양성훈(Seong-Hun Yang),오승진(Seung-Jin Oh),강진범(Jinbeom Kang) 한국지능정보시스템학회 2022 지능정보연구 Vol.28 No.1
Recently, the amount of video data collected from smartphones, CCTVs, black boxes, and high-definition cameras has increased rapidly. According to the increasing video data, the requirements for analysis and utilization are increasing. Due to the lack of skilled manpower to analyze videos in many industries, machine learning and artificial intelligence are actively used to assist manpower. In this situation, the demand for various computer vision technologies such as object detection and tracking, action detection, emotion detection, and Re-ID also increased rapidly. However, the object detection and tracking technology has many difficulties that degrade performance, such as re-appearance after the object’s departure from the video recording location, and occlusion. Accordingly, action and emotion detection models based on object detection and tracking models also have difficulties in extracting data for each object. In addition, deep learning architectures consist of various models suffer from performance degradation due to bottlenects and lack of optimization. In this study, we propose an video analysis system consists of YOLOv5 based DeepSORT object tracking model, SlowFast based action recognition model, Torchreid based Re-ID model, and AWS Rekognition which is emotion recognition service. Proposed model uses single-linkage hierarchical clustering based Re-ID and some processing method which maximize hardware throughput. It has higher accuracy than the performance of the re-identification model using simple metrics, near real-time processing performance, and prevents tracking failure due to object departure and re-emergence, occlusion, etc. By continuously linking the action and facial emotion detection results of each object to the same object, it is possible to efficiently analyze videos. The re-identification model extracts a feature vector from the bounding box of object image detected by the object tracking model for each frame, and applies the single-linkage hierarchical clustering from the past frame using the extracted feature vectors to identify the same object that failed to track. Through the above process, it is possible to re-track the same object that has failed to tracking in the case of re-appearance or occlusion after leaving the video location. As a result, action and facial emotion detection results of the newly recognized object due to the tracking fails can be linked to those of the object that appeared in the past. On the other hand, as a way to improve processing performance, we introduce Bounding Box Queue by Object and Feature Queue method that can reduce RAM memory requirements while maximizing GPU memory throughput. Also we introduce the IoF(Intersection over Face) algorithm that allows facial emotion recognized through AWS Rekognition to be linked with object tracking information. The academic significance of this study is that the two-stage re-identification model can have real-time performance even in a high-cost environment that performs action and facial emotion detection according to processing techniques without reducing the accuracy by using simple metrics to achieve real-time performance. The practical implication of this study is that in various industrial fields that require action and facial emotion detection but have many difficulties due to the fails in object tracking can analyze videos effectively through proposed model. Proposed model which has high accuracy of retrace and processing performance can be used in various fields such as intelligent monitoring, observation services and behavioral or psychological analysis services where the integration of tracking information and extracted metadata creates greate industrial and business value. In the future, in order to measure the object tracking performance more precisely, there is a need to conduct an experiment using the MOT Challenge dataset, which is data used by many international conferences. We will invest
분산전원과 에너지저장을 사용한 직류급전 시스템의 Autonomous Operation을 위한 Droop 제어
이지헌(Ji-Heon Lee),정종규(Jong-Kyu Jeong),차민영(Min-Young Cha),오승진(Seong-Jin Oh),김종원(Jong-Won Kim),한병문(Byung-Moon Han) 대한전기학회 2010 대한전기학회 학술대회 논문집 Vol.2010 No.10
This paper describes a droop control method for the autonomous operation of DC distribution system using distributed generations and energy storage. The method suppress the circulating current, and each unit could be controlled autonomously without communication system. Detailed model of wind power generation. photo-voltaic generation, fuel-cell generation and battery was implemented with the user-defined model of PSCAD/EMTDC software that is coded with C-language. The simulation and experimental results confirms that the proposed DC distribution system make it feasible to provide power to the load stably and verify effectiveness of the proposed method.