Earticle

초록

영어

Recently, research on pedestrian action recognition from the vehicle’s viewpoint is being studied in many ways. The information about pedestrian action classification is very important for autonomous driving to determine safe path planning and avoid accidents. To provide a computationally efficient solution to pedestrian action recognition, this paper proposes a multi-head CNN model to currently extract multi-actions of pedestrians from the unified model. This model consists of one pre-trained backbone network and two head networks. One head network classifies Gait (walking/standing) and the second classifies Attention (looking/non-looking) of pedestrians. The proposed model offers a lighter model with smaller memory, faster processing speed, and alleviates data imbalance problem – a common problem found in most of dataset – leading to improved accuracy.

목차

Abstract
I. INTRODUCTION
II. RELATED WORK
III. PROPOSED METHOD
1. Data Augmentation
2. Proposed Multi-Head CNN Model
IV. EXPERIMENTAL RESULTS
V. CONCLUSION
REFERENCE

키워드

저자

• TaeMi Park [ Chungbuk National University College of Electrical and Computer Engineering Cheongju, Korea ]
• HyungWon Kim [ Chungbuk National University College of Electrical and Computer Engineering Cheongju, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004