Earticle

초록

영어

Multi-object tracking techniques are receiving increasing attention due to the growing demand of autonomous driving. Recently, the performance of multi-object tracking has been improved significantly thank to deep learning technique. Most of multi-object tracking methods based on deep learning, however, are highly prone to frequent tracking losses and track-ID switching in case of limited viewpoint and occluded objects. To alleviate this problem, we propose a multi-camera Collaborate Multi Object Tracking (CMOT) method which performs online association of multiple tracked vehicles from stereo vision camera. CMOT not only provides global tracking IDs between multiple cameras but also helps reduce the problem of ID switching compared with the conventional multi-object tracking based on single camera. It can, therefore, improve the overall performance of multi-vehicle tracking compared to each individual camera. We demonstrate the multi-object tracking performance of the proposed method using stereo images of the KITTI dataset.

목차

Abstract
I. INTRODUCTION
II. MAIN ALGORITHM
III. EXPERIMENTS
A. Evaluation metrics
B. Implementation details
C. Preliminary results
IV. CONCLUSTION
REFERENCES

키워드

저자

• Phong Phu Ninh [ Department of Electronic Engineering Chungbuk National University Cheongju, South Korea ]
• Hyungwon Kim [ Department of Electronic Engineering Chungbuk National University Cheongju, South Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004