• Title/Summary/Keyword: OC-SORT

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A climbing movement detection system through efficient cow behavior recognition based on YOLOX and OC-SORT (YOLOX와 OC-SORT 기반의 효율적인 소 행동 인식을 통한 승가 운동 감지시스템)

  • LI YU;NamHo Kim
    • Smart Media Journal
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    • v.12 no.7
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    • pp.18-26
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    • 2023
  • In this study, we propose a cow behavior recognition system based on YOLOX and OC-SORT. YOLO X detects targets in real-time and provides information on cow location and behavior. The OC-SORT module tracks cows in the video and assigns unique IDs. The quantitative analysis module analyzes the behavior and location information of cows. Experimental results show that our system demonstrates high accuracy and precision in target detection and tracking. The average precision (AP) of YOLOX was 82.2%, the average recall (AR) was 85.5%, the number of parameters was 54.15M, and the computation was 194.16GFLOPs. OC-SORT was able to maintain high-precision real-time target tracking in complex environments and occlusion situations. By analyzing changes in cow movement and frequency of mounting behavior, our system can help more accurately discern the estrus behavior of cows.

Deep Learning-Based Roundabout Traffic Analysis System Using Unmanned Aerial Vehicle Videos (드론 영상을 이용한 딥러닝 기반 회전 교차로 교통 분석 시스템)

  • Janghoon Lee;Yoonho Hwang;Heejeong Kwon;Ji-Won Choi;Jong Taek Lee
    • IEMEK Journal of Embedded Systems and Applications
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    • v.18 no.3
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    • pp.125-132
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    • 2023
  • Roundabouts have strengths in traffic flow and safety but can present difficulties for inexperienced drivers. Demand to acquire and analyze drone images has increased to enhance a traffic environment allowing drivers to deal with roundabouts easily. In this paper, we propose a roundabout traffic analysis system that detects, tracks, and analyzes vehicles using a deep learning-based object detection model (YOLOv7) in drone images. About 3600 images for object detection model learning and testing were extracted and labeled from 1 hour of drone video. Through training diverse conditions and evaluating the performance of object detection models, we achieved an average precision (AP) of up to 97.2%. In addition, we utilized SORT (Simple Online and Realtime Tracking) and OC-SORT (Observation-Centric SORT), a real-time object tracking algorithm, which resulted in an average MOTA (Multiple Object Tracking Accuracy) of up to 89.2%. By implementing a method for measuring roundabout entry speed, we achieved an accuracy of 94.5%.

A study of the electrical characteristics changes of PV cell at high temperature (태양전지 셀의 고온에 의한 전기적 특성 변화 연구)

  • Jung, Tae-Hee;Shin, Jun-Oh;Kim, Tae-Bum;Kang, Gi-Hwan;Ahn, Hyung-Keun;Han, Deuk-Young
    • 한국신재생에너지학회:학술대회논문집
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    • 2009.11a
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    • pp.387-389
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    • 2009
  • PV module is manufactured by several steps such as cell sort, tabbing & string, lay-up, lamination processes. In oder to manufacture PV module, solar cell must be placed in high temperature. Soldering Process in high temperature is important because it directly influences electric output performance changes of solar cell in solar cell module. We consider applying momentary high temperature, while soldering solar cell, and expect change electric characteristics of PV module. In this paper, we measure electric output characteristics of solar cells after those are applied with high temperature changes for two seconds. From these results, we confirm with application of high temperature, $I_{sc}$ increase and $V_{oc}$ slightly decreases.

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The Crowd Density Estimation Using Pedestrian Depth Information (보행자 깊이 정보를 이용한 군중 밀집도 추정)

  • Yu-Jin Roh;Sang-Min Lee
    • Proceedings of the Korea Information Processing Society Conference
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    • 2023.11a
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    • pp.705-708
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    • 2023
  • 다중밀집 사고를 사전에 방지하기 위해 군중 밀집도를 정확하게 파악하는 것은 중요하다. 기존 방법 중 일부는 군중 계수를 기반으로 군중 밀집도를 추정하거나 원근 왜곡이 있는 데이터를 그대로 학습한다. 이 방식은 물체의 거리에 따라 크기가 달라지는 원근 왜곡에 큰 영향을 받는다. 본 연구는 보행자 깊이 정보를 이용한 군중 밀집도 알고리즘을 제안한다. 보행자의 깊이 정보를 계산하기 위해 편차가 적은 머리 크기를 이용한다. 머리를 탐지하기 위해 OC-Sort를 학습모델로 사용한다. 탐지된 머리의 경계박스 좌표, 실제 머리 크기, 카메라 파라미터 등을 이용하여 보행자의 깊이 정보를 추정한다. 이후 깊이 정보를 기반으로 밀도 맵을 추정한다. 제안 알고리즘은 혼잡한 환경에서 객체의 위치와 밀집도를 정확하게 분석하여 군중밀집 사고를 사전에 방지하는 지능형 CCTV시스템의 기반 기술로 활용될 수 있으며, 더불어 보안 및 교통 관리 시스템의 효율성을 향상하는 데 중요한 역할을 할 것으로 기대한다.

Luteolin inhibits H2O2-induced cellular senescence via modulation of SIRT1 and p53

  • Zhu, Ri Zhe;Li, Bing Si;Gao, Shang Shang;Seo, Jae Ho;Choi, Byung-Min
    • The Korean Journal of Physiology and Pharmacology
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    • v.25 no.4
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    • pp.297-305
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    • 2021
  • Luteolin, a sort of flavonoid, has been reported to be involved in neuroprotective function via suppression of neuroinflammation. In this study, we investigated the protective effect of luteolin against oxidative stress-induced cellular senescence and its molecular mechanism using hydrogen peroxide (H2O2)-induced cellular senescence model in House Ear Institute-Organ of Corti 1 cells (HEI-OC1). Our results showed that luteolin attenuated senescent phenotypes including alterations of morphology, cell proliferation, senescence-associated 𝛽-galactosidase expression, DNA damage, as well as related molecules expression such as p53 and p21 in the oxidant challenged model. Interestingly, we found that luteolin induces expression of sirtuin 1 in dose- and time-dependent manners and it has protective role against H2O2-induced cellular senescence by upregulation of sirtuin 1 (SIRT1). In contrast, the inhibitory effect of luteolin on cellular senescence under oxidative stress was abolished by silencing of SIRT1. This study indicates that luteolin effectively protects against oxidative stress-induced cellular senescence through p53 and SIRT1. These results suggest that luteolin possesses therapeutic potentials against age-related hearing loss that are induced by oxidative stress.