Human behavior is an essential component of social interaction and is of great significance to identify and analyze human behaviors in a variety of fields. Due to the rapid development of computer vision and machine learning technology, machine with intelligence has started replacing human beings to observe, perceive and analyze the explosive growth of image and video data. Computer vision and machine learning-based human behavior recognition is one of these tasks, which has become a particularly hot research topic in many different fields, such as intelligent monitoring, human-computer interaction, smart home, virtual reality, and medical diagnosis. In this study, we survey systematically the popular methods, algorithms, models and well-known action datasets in human behavior analysis in the past two decades. In addition, the advantages and disadvantages of the methods are discussed and propitious future research directions are also presented. The results of this survey reveal that paradigms of human behavior analysis is being shifted from traditional RGB to RGB-D, from deep learning to more intelligent and automated deep reinforcement learning, and from fixed camera devices to portable devices and channel state information (CSI), and paradigms based on automated deep reinforcement learning and portable devices and CSI would become some hot topics for future research on human behavior analysis.
Human action recognition (HAR) plays an important role in social interaction in various fields. This study proposes a light-weight skeleton and two-layer bidirectional LSTM-based Seq2Seq model (SB2_Seq2Seq) for HAR to trade off recognition accuracy, users’ privacy and computer resource usage. An experiment was conducted to compare the proposed SB2_Seq2Seq with other skeleton-based Seq2Seq models and non-skeleton RGB video frame-based LSTM, CNN and seq2seq models. The UCF50 dataset was used for model evaluation, where 60%, 20% and 20% for model training, validation and testing, respectively. The experimental results show that the proposed model achieves 93.54% accuracy with 0.0214 Mean Square Error (MSE), suggesting that the proposed model outperforms all the other models. Besides, it also shows that the proposed model achieves state-of-the-art accuracy compared with state-of-the-arts methods in literature.
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