Predicting Human Behavior Using 3D Loop ResNet
Yoshiki Kakamu, Kazuhiro Hotta · 2022 26th International Conference on Pattern Recognition (ICPR) · 2022
In this research, we would like to predict human behavior from video images to help humans in the future medical and nursing care fields. As a preliminary step, we will predict human actions. As a baseline, we used the 3D ResNet, which can handle both temporal and spatial features and can identify human actions with high accuracy. To improve the feature representation of conventional 3D ResNet for small action, we propose a method using loop feature extraction based on Convolutional LSTM in a residual block. We conducted experiments using the UCF101, Kinetics-400, and HMDB-51 datasets. As a result, we confirmed that our method with the introduction of the loop mechanism can obtain higher accuracy than the conventional 3D ResNet. Next, we created our own data set for predicting human behavior and used it for evaluation. We were able to achieve high accuracy, indicating that it is possible to predict behavior to some extent.