Abstract
This paper represents the recognition of group activity in public areas, considering personal actions and interactions between people from the field of computer vision. Modeling the interaction relationships between multiple people is essential for recognizing group activity in the video scene. In artificial intelligence applications, identifying group activities based on human interaction is often a challenging task. This paper proposed a model that formulates a group action context (GAC) descriptor. The descriptor was developed by integrating the focal person action descriptor and interaction joint context descriptor of nearby people in the video frame. The model used an efficient optimization principle based on machine learning to learn the discriminative interaction context relations between multiple persons. The proposed novel group action context descriptor is classified by support vector machine (SVM) to recognize group activity. The proposed technique effectiveness is evaluated for group activity recognition by performing experiments on a publicly available collective activity dataset. The proposed approach infers a group action class when multiple persons are together in the video sequence, especially when the interaction between people is confusing. The overall group action recognition model is interrelated with a baseline model to estimate the performance of interaction context information. The experimental result of the proposed group activity recognition model is comparable and outperforms the previous methods.
Highlights
Multiple person activity recognition algorithms have established significant attention in the field of computer vision as well as artificial intelligence
Is section describes the strategy of the group activity recognition method. us, the group action context (GAC) descriptor is formulated from the people interaction in a scene and this descriptor is classified into group activity category by using a multiclass support vector machine (SVM) classifier
In the proposed group interaction model, the assumption is that the focal person action descriptor would be extremely related to the interaction context as a group action, which is affected by the multiple people pose and actions in the video frame
Summary
Multiple person activity recognition algorithms have established significant attention in the field of computer vision as well as artificial intelligence. Group activity recognition recognizes actions that are performed by multiple people. The interaction joint context is used to develop an innovative group action context (GAC) descriptor model for efficient group activity recognition process. An algorithm is developed based on the dominant pose and action to determine the interaction within multiple people in the video scenes. It proposes a novel group action context descriptor (GAC) that encodes the interaction between joint context and action descriptor of the focal the person.
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