부산대학교 도서관

Frequent uncertain falls is one of the common cause of injury among elderly adults and persons suffering from the neurological disorder. It will be costlier to go through $24\times 7$ medical monitoring if we monitor a person suffering from the early stage of the neurological disorder. An “uncertain” action classification model can be a less costly and easily scalable. It can help to regularly monitor a person suffering from neurological declines and how frequent it relapse. In this article, we propose a video-based action recognition with fall detection architecture, FallNet, which learns the features of uncertain actions related to day-to-day activities. FallNet first incorporates semantic supervision using the per-class weight of uncertain action through class-wise weighted focal loss. It addresses both the class imbalance problem and the weak interclass separability issue. We design a joint training model to train the overall architecture efficiently in an end-to-end manner. We utilize benchmark data sets, OOPS, HMDB51, and Kinetics-600, for experimentation that has less falling action videos. Therefore, we have collected videos to create a data set, denoted by FallAction, that consists of different 15 falling action classes with an average of 100 videos per class. The proposed network gain an accuracy of 13.2% in OOPs, 2% in HMDB51, and 0.2% in Kinetics-600 data set.