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Group behavior recognition is an important research problem that is widely used in the field of computer vision and needs to be solved urgently. With the development of deep neural networks, the breadth and depth of group behavior recognition and understanding are also expanding. By investigating the research literature of group behavior recognition in the past ten years, the problem definition of the current group behavior recognition research is determined; the existing problems and challenges of group behavior recognition research are pointed out; under the deep learning network architecture, it is described that from the early days only the group behavior recognition For classification and recognition, the development process of group behavior recognition algorithms that focus more on understanding the details of activities in behavior groups; focuses on the convolutional neural network CNN/3DCNN, the two-stream network Two-Stream Network, and the recurrent neural network RNN/LSTM Based on the network architectures such as Transformer, the core network architecture and main research ideas of mainstream group behavior recognition algorithms, the recognition effects of each algorithm on common public data sets are compared; group behavior types and individual behavior categories are marked. Commonly used group behavior datasets with multi-level labels are combed and compared. It is hoped that through the objective discussion and analysis of the advantages and disadvantages of various algorithms, readers will be prompted to propose new ideas or new problems in the study of group behavior recognition. In the conclusion, the future development of group behavior analysis is prospected, and new research directions are expected to be inspired.
http://fcst.ceaj.org/CN/abstract/abstract2962.shtml
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