Convolutional Attention-Based Bidirectional Recurrent Neural Network for Human Action Recognition

Abstract

Human activity recognition (HAR) technology plays a major role in today's world and is used in detecting human actions and poses in real-time. In the past, researchers employed statistical machine learning methods to build and extract attributes of various movements manually. However, typical techniques are becoming increasingly ineffective in the face of exponentially increasing waveform data that lacks unambiguous principles. With the advancement of deep learning technology, manual feature extraction is no longer required, and performance on challenging human activity recognition problems can be improved. However, various deep learning models have problems such as time consumption, inaccuracy, and the vanishing gradient problem. Therefore, to solve these problems, the proposed study used a deep convolutional attention-based bidirectional recurrent neural network to detect human activities in the provided samples. The input images are first pre-processed using an adaptive bilateral filtering approach to improve their quality and remove image noise. Then, the crucial features are recovered using the convolutional neural network (CNN) based encoder-decoder model. Finally, a deep convolutional attention-based bidirectional recurrent neural network is used to identify human activities. The model recognizes human actions with higher effectiveness and lower latency. The human behaviors are identified using the HMDB51 dataset. The proposed model acquired the highest accuracy of 95.46%, which is 10.51% superior to multi-layer perceptron (MLP), 6.99% superior to CNN, 12.76% superior to long short-term memory (LSTM), 5.59% superior to Bidirectional LSTM (BiLSTM), and 4.82% superior to CNN-LSTM, respectively.