Abstract
Multiperson activity recognition is a pivotal branch as well as a challenging topic of human action recognition research. This paper adopts a hybrid learning model to the spatio-temporal relationship and occlusion relationship among multiple people. Initially, this paper builds up an active multiperson interaction relationship estimation framework model to capture interpersonal spatio-temporal relation. This model incorporates the interaction relationship estimation framework with the multiperson relationship network. On this ground, it automatically learns from the human-computer interaction dataset in an end-to-end manner and performs reasoning with standard matrix operations. Secondly, this paper proposed an adaptive occlusion state behavior recognition method derived from the semantic knowledge model to ravel out the concern of occlusion and self-occlusion in human action recognition. Then, Petri Nets are used to recognize multiperson interactive actions. This model has been through extensive experiments on the TV interaction dataset, Vlog dataset, AVA dataset, and MLB-YouTube dataset, experimental results have proved that the recognition performance of this model is superior than the other available models. This paper prospects and summarizes the estimation framework of the interaction relationship and occlusion semantic-knowledge relationship. Experimental results suggest that the proposed method in the paper could capture the discriminative relation information for multiperson interactive activity recognition, which further validates the efficiency of the hybrid learning model.
Highlights
Human interactive activity involves social behaviors and interactive behaviors. e former refers to individual behaviors, yet it takes other individuals’ behaviors into account; the latter refers to group behaviors for a shared goal
In order to recognize the interaction relationship of multiperson behavior, we developed a multiperson relation method in which we defined three indexes [19]: movement time (MT), nonoverlapped movement time (NOMT), and group movement time (GMT)
We build a flexible and efficient model of the multiperson interaction relation estimation framework to capture the appearance and position relation of people, and a new reasoning approach is formed. e temporal consistency is handled via a person-level matching recurrent neural network
Summary
Human interactive activity involves social behaviors and interactive behaviors. e former refers to individual behaviors, yet it takes other individuals’ behaviors into account; the latter refers to group behaviors for a shared goal. Collective intelligence is built, aiming to jointly reason multiple individuals’ behaviors On this ground, the paper proposed a method to recognize group behaviors which allow us to locate and narrate the interactive and collective actions of each individual in the context. We have observed that the current trend [17,18,19,20,21] of tackling the problem of multiperson interactive activity recognition is to develop a model or framework with increasing complexity to jointly learn more subtasks simultaneously (e.g., detection, tracking, pose estimation, appearance modelling, and interaction). General conclusions are made and possible further improvements on this research are stated
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