Spatio-temporal interest point feature encoding method in human motion recognition

Abstract

The invention discloses a spatio-temporal interest point feature encoding method in human motion recognition. Human motion recognition in a video has broad application prospects in aspects of intelligent monitoring, video retrieval and the like. A human motion recognition method based on spatio-temporal interest point features has the advantages of simple extraction, strong anti-interference ability, good robustness and the like, and thus wide attention is got. However, during a process of encoding the spatio-temporal interest point features through vector quantization so as to acquire video representation vectors, problems of large representation errors, weak encoding discrimination ability and lost spatial position information exist. In order to solve the above problems, the invention discloses spatial regulation local constraint encoding algorithm. During a feature encoding process, local constraints are introduced for reducing representation errors, spatial regulation is introduced for enhancing discrimination of an encoding result and using the spatio-temporal interest point features for the spatial position information, and finally, the precision of human motion recognition is improved.

Description

technical field [0001] The invention mainly relates to the field of digital video content understanding and analysis, in particular to a feature encoding method of spatio-temporal interest points in human action recognition. Background technique [0002] Human action recognition in video is to extract visual information that can describe the characteristics of human action actions from video sequences, and use machine learning algorithms to classify these information to achieve the purpose of identifying human action actions. It has broad application prospects in intelligent monitoring, video retrieval, robot control, etc. [0003] Human action recognition in video includes video feature extraction, action representation, action classification and other links. The video features used for action recognition mainly include: human body model, global features and local features. Compared with human body model and global features, local features have the advantages of simple ex...

AI Technical Summary

Problems solved by technology

Second, the distribution of spatio-temporal interest point features in its representation space behaves as a nonlinear manifold (3), and vector quantization is difficult to model the nonlinear manifold (3) structure

However, vector quantization only utilizes their feature description information, while ignoring their location information in the human body area, thus, it cannot model the spatial location distribution of spatio-temporal interest point features

Among them, the first two shortcomings can be summarized as the problem of large representation error, which is manifested as large quantization error and poor accuracy of nonlinear manifold representation.

The third deficiency can be considered as the encoding fuzzy problem, which is manifested as giving the same encoding result to the spatio-temporal interest point features from different human body parts

The fourth is the problem of missing spatial position relationship, which is manifested as the inability to model the spatial position relationship of spatio-temporal interest point features

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