Human Action Recognition Using Key-Frame Attention-Based LSTM Networks

Human action recognition is a classical problem in computer vision and machine learning, and the task of effectively and efficiently recognising human actions is a concern for researchers. In this paper, we propose a key-frame-based approach to human action recognition. First, we designed a key-frame attention-based LSTM network (KF-LSTM) using the attention mechanism, which can be combined with LSTM to effectively recognise human action sequences by assigning different weight scale values to give more attention to key frames. In addition, we designed a new key-frame extraction method by combining an automatic segmentation model based on the autoregressive moving average (ARMA) algorithm and the K-means clustering algorithm. This method effectively avoids the possibility of inter-frame confusion in the temporal sequence of key frames of different actions and ensures that the subsequent human action recognition task proceeds smoothly. The dataset used in the experiments was acquired with an IMU sensor-based motion capture device, and we separately extracted the motion features of each joint using a manual method and then performed collective inference.