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Trajectory Extraction and Utilization Method Based on Visual SLAM and Object Tracking for Traffic Accident Prediction

첫 페이지 보기
  • 발행기관
    한국차세대컴퓨팅학회 바로가기
  • 간행물
    한국차세대컴퓨팅학회 학술대회 바로가기
  • 통권
    The 9th International Conference on Next Generation Computing 2023 (2023.12)바로가기
  • 페이지
    pp.161-164
  • 저자
    Jikyu Park, Jongho Won, Deok-Hwan Kim
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A448141

원문정보

초록

영어
In autonomous driving, environmental perception and decision-making are technologies that acquire signals based on various sensors and generate information that enables obstacle avoidance, emergency stops, and path planning. Such environmental perception technologies are limited in that they depend on expensive sensors such as LIDAR and RADAR, so research on environmental perception technologies using only cameras is actively being conducted. Studies predicting traffic accidents based on dashcam footage have also been performed as part of such research. This is challenging because only limited forward-looking footage can be acquired, and the surrounding environment is dynamic and changes quickly, making analysis difficult. Existing research has focused on learning the spatialtemporal feature representation to solve these problems. This paper extracts the trajectories of the ego-vehicle and surrounding vehicles using Visual SLAM and Multiple Object Tracking algorithms and uses them as inputs to the graph convolutional neural network(GCN) to learn the spatialtemporal feature representation. In addition, the features learned through the GCN are used as inputs to a bayesian neural network(BNN) to predict the probability of accidents, and its ability to predict accidents in advance has been verified by comparison with existing studies.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
A. Visual SLAM
B. Multi-Object Tracking
III. PROPOSED METHOD
A. Camera calibration
B. Ego-Motion estimation
C. Detection and Tracking of Surrounding Vehicles
D. Global Frame Trajectory
IV. EXPERIMENTS
A. USED DATASET
B. EXPERIMENTAL RESULTS
V. CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

Traffic Accident Prediction Visual SLAM Multi-Object Tracking

저자

  • Jikyu Park [ Department of Electrical and Computer Engineering Inha University Incheon, South Korea ]
  • Jongho Won [ Department of Electrical and Computer Engineering Inha University Incheon, South Korea ]
  • Deok-Hwan Kim [ Department of Electrical and Computer Engineering Inha University Incheon, South Korea ] Corresponding Author

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    한국차세대컴퓨팅학회 [Korean Institute of Next Generation Computing]
  • 설립연도
    2005
  • 분야
    공학>컴퓨터학
  • 소개
    본 학회는 차세대 PC 및 그 관련분야의 학술활동을 통하여 차세대 PC의 학문 및 기술발전을 도모하고 산업발전 및 국제협력 증진을 목적으로 한다.

간행물

  • 간행물명
    한국차세대컴퓨팅학회 학술대회
  • 간기
    반년간
  • 수록기간
    2021~2025
  • 십진분류
    KDC 566 DDC 004

이 권호 내 다른 논문 / 한국차세대컴퓨팅학회 학술대회 The 9th International Conference on Next Generation Computing 2023

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