Single-Object Fall Detection Using Pose Estimation 3D Coordinates via Generating 3D Object Coordinates from 2D Object Coordinates
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Object fall detection is one of the significant applications in pose estimation. Traditional approaches heavily rely on fully mature neural networks, which may be complex to implement and resource-intensive. In this paper, we suggest a simple approach to detect Human fall on a single object using a mathematical comparison mechanism from 3D pose estimation landmarks lifted from 2D landmarks, suitable for low-specification systems. Our research focuses on dimensional lifting in 2D to 3D pose estimation based on object tracking—to adapt mapping 2D toons to 3D toons. For future research, we aim to develop a lightweight neural network for enhanced performance from ensemble effects and complex action detection. We also plan on enhancing the algorithm for multi-person detection. At this moment, our research extends into 3D toon generation. Our method achieves an F1-score of 94.7% (accuracy of 96.7%) with 28.2 FPS in detecting falls from webcam footage in a controlled environment.
Abstract
Object fall detection is one of the significant applications in pose estimation. Traditional approaches heavily rely on fully mature neural networks, which may be complex to implement and resource-intensive. In this paper, we suggest a simple approach to detect Human fall on a single object using a mathematical comparison mechanism from 3D pose estimation landmarks lifted from 2D landmarks, suitable for low-specification systems. Our research focuses on dimensional lifting in 2D to 3D pose estimation based on object tracking—to adapt mapping 2D toons to 3D toons. For future research, we aim to develop a lightweight neural network for enhanced performance from ensemble effects and complex action detection. We also plan on enhancing the algorithm for multi-person detection. At this moment, our research extends into 3D toon generation. Our method achieves an F1-score of 94.7% (accuracy of 96.7%) with 28.2 FPS in detecting falls from webcam footage in a controlled environment.