Human body behavior recognition method based on motion cooperation space

Abstract

The invention discloses a human body behavior identification method based on a motion cooperation space. The method comprises the following steps: S1, respectively carrying out key frame extraction based on a motion state measurement coefficient on an initial skeleton sequence and a depth map sequence; s2, extracting motion coordination space vectors from the skeleton sequence processed in the step S1, and splicing the motion coordination space vectors into motion coordination space features; extracting DMM features from the depth map sequence processed in the step S1 to obtain a depth motion map; and S3, inputting the deep motion map and the motion collaborative spatial features into a deep network at the same time, and performing score fusion. Based on the idea of multi-modal fusion, skeleton data and depth data are combined, so that the data are more complete, complementation of various heterogeneous information is realized, redundancy among modals is eliminated, a new behavior recognition method system is established, and a new thought and theoretical basis are provided for research and application of a human body behavior recognition method.

Description

technical field [0001] The invention relates to a method for recognizing human behavior, in particular to a method for recognizing human behavior based on a motion collaborative space. Background technique [0002] Human behavior recognition is one of the research hotspots in the field of computer vision, and many research results have been widely used in image analysis, human-computer interaction, intelligent monitoring, video retrieval, somatosensory games, and health detection. [0003] The related research on behavior recognition can be traced back to an experiment of Johansson in 1973 (GUNNARJOHANSSON.(1973) Visual perception of biological motion and a model for its analysis In Perception&Psychophysics 1973.Vol.14.No.2.201·211), by using 10- The movement of 12 human key nodes is used to describe human motion and identify human behavior. In the early days, behavior recognition research was mainly based on RGB video sequences, but it was limited by factors such as illumi...

AI Technical Summary

Problems solved by technology

However, these recognition methods are still relatively simple, and the application of bone data cannot well reflect the integrity and coordination of human movements.

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