Two-person interaction behavior recognition method based on maximum interval Markov network model

Abstract

Two-person interactive behavior recognition method based on the maximum interval Markov network model, including: using a structured model implemented by a maximum interval method to model high-level semantics, and manually setting a small number of feature templates to represent interactive behavior; including single-person semantics Recognition and interactive behavior recognition are two steps; single-person tracking is carried out on the double-person database, and the two persons in the interaction are respectively obtained, and the action context descriptor that can represent the local appearance and local movement is extracted as the underlying feature for the single person, and the method of metric learning is adopted Obtain the semantics of single-person atomic behavior; combine the semantics of single-person atomic behavior and its associated interactive behavior with feature templates, train a structured maximum-margin Markov network to obtain a model of modeling interactive behavior, and use this model to infer the interactive behavior of two people . This method can effectively eliminate the background interference in the tracking stage, play an error-correcting role in the interactive modeling, and the recognition effect is good.

Description

technical field [0001] The invention belongs to the field of image processing technology and pattern recognition, in particular to a double-person interactive behavior recognition method based on a maximum interval Markov network model. Background technique [0002] Human behavior recognition, especially the recognition of the most common interaction between people in daily life, is of great significance for intelligent monitoring, and it is a hot spot and difficulty that has attracted much attention in the fields of computer vision and pattern recognition. However, the diversity of objective environments and the complexity of human motion make human behavior recognition extremely difficult. At present, the methods for interactive behavior recognition are mainly reflected in the modeling of the spatial and temporal structure of the underlying features, and there are few studies on the high-level semantic description in behavior recognition. Most of the existing high-level be...

AI Technical Summary

Problems solved by technology

However, the diversity of objective environments and the complexity of human motion make human behavior recognition extremely difficult

At present, the methods for interactive behavior recognition are mainly reflected in the modeling of the spatial and temporal structure of the underlying features, and there are few studies on the high-level semantic description in behavior recognition. Most of the existing high-level behavior semantic modeling methods are relatively complicated. , a large number of rules need to be manually set, so how to obtain behavioral semantics that humans can directly understand based on the underlying features is still a challenge

[0003] Among the existing two-person interactive behavior recognition algorithms based on high-level semantic description, there are a series of algorithms based on random grammar and Markov logic network model. Random grammar is proposed by Ryoo and Aggarwal to describe the high-level semantics of group behavior. Grammar-based methods can effectively model the internal structure of complex behaviors, but most of these methods need to manually set all possible production rules, which is too much work, and traditional knowledge-based and logical reasoning-based methods can only Precise reasoning of knowledge, powerless against errors and uncertainties in input data

Markov Logic Network (MLN for short) is a combination of Markov Network and first-order logic knowledge base, which can not only model flexibly, but also deal with uncertainty. A large number of rules need to be manually set, and the model is complex

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