Objectives: To provide comprehensive review of different group activity recognition methods, categorize them and provide path to new researcher in this domain. Methods/Statistical Analysis: Different methods of group activity recognition categorized and analyzed according to hand-crafted and learned feature descriptors. Pros and cons of each method are presented. Methods are analyzed in detailed by finding its local level features to global level feature descriptors used along with performance on benchmark dataset. Findings: Different models of group activity recognition are characterized as per the capabilities of the defined model considering individual pose of person, atomic activity of person, person-person interaction, person-group interaction, group-group interaction, uses of temporal information, and recognition of group activity frame wise or video wise. This comprehensive review provides brief information about group activity recognition methods and can be used as brief literature review to the researcher seeking the facts and findings in the field of computer vision in group activity recognition. Applications/Improvements: This reviews help in different applications of human activity analysis, mainly in group activity recognition and the models described here can be used in different applications such running or walking on pathways, waiting at public places, queuing in line in group and many more group activity applications for further enhancement.
Keywords
Context Model, Convolution Neural Network, Group Activity Recognition, Group Descriptor, Interaction Model
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