정적 영상 기반 행동 인식을 활용한 낙상 감지 기술 및 운전자 부주의 감지 기술 개발

Abstract

Given image-level training data, a single test image can be used to identify a person’s actions or behaviors. This study highlights how the use of keypoint-based representation from single images can simplify the visual patterns of image events. Over the past few decades, deep learning techniques have successfully been applied in data-driven keypoint detection and skeletal analysis. While some engineered features from RGB or RGB-D datasets fail in image recognition applications due to ambiguous lightning conditions, previous knowledge from machine learning experimentation on large-scale data can be transferred into a new domain to improve performance. This idea of applying previously trained data can be applied to other applications as well. By adopting pre-trained convolutional neural network (CNN) models utilizing action recognition, new applications can be developed for events such as fall detection and driver drowsiness detection. Using state-of-the-art CNNs as a deep feature extractor to extract important key points of a human body or face, the geometric relationship of the predicted joints or facial features can be analyzed to aid in the design of hazardous event detection methods. The methods in this report were validated with publicly available datasets and successfully demonstrated in real-time. Two different data acquisition systems were used to train and validate these methods using real-world images and qualitatively verify them with a sequence of static images. Details of the algorithms and two practical applications are also outlined. The approach used in this study is scalable and can be extended to other hazardous event detection methods in the future.