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Activity recognition has become an important function in many emerging computer vision applications e.g. automatic video surveillance system, human-computer interaction application, and video recommendation system, etc. In this paper, we propose a novel semantics based group activity recognition scheme, namely SBGAR, which achieves higher accuracy and efficiency than existing group activity recognition methods. SBGAR consists of two stages: in stage I, we use a LSTM model to generate a caption for each video frame; in stage II, another LSTM model is trained to predict the final activity categories based on these generated captions. We evaluate SBGAR using two well-known datasets: the Collective Activity Dataset and the Volleyball Dataset. Our experimental results show that SBGAR improves the group activity recognition accuracy with shorter computation time compared to the state-of-the-art methods.
Li et al. (Sun,) studied this question.