Angle independence-based skeleton behavior recognition method, system and device

Abstract

The invention relates to the field of human body behavior recognition, in particular to an angle independence-based skeleton behavior recognition method, system and device, and aims to improve the accuracy of angle-independent skeleton behavior recognition. The angle independence-based skeleton behavior recognition method comprises the steps of designing a specific visual angle sub-network on thebasis of a skeleton sequence of each visual angle, focusing on key joint points and key frames through space domain attention and time domain attention modules respectively, and learning a discrimination characteristic of each visual angle sequence through a multi-layer long-short-term memory network; serially connecting output characteristics of all the specific visual angle sub-networks to serveas an input of a public sub-network, further learning angle-independent characteristics through a bidirectional long-short-term memory network, and focusing on a key visual angle through a visual angle attention module; and proposing a regularization cross entropy loss function for promoting the modules of the network to jointly learn. According to the skeleton behavior recognition method, systemand device, the recognition accuracy is effectively improved, and visual angle characteristics with relatively numerous learning information can be automatically focused.

Description

technical field [0001] The invention relates to the field of human behavior recognition, in particular to a skeleton behavior recognition method, system and equipment based on angle independence. Background technique [0002] As an important research field of computer vision, human behavior recognition is a system that classifies and recognizes human behavior through input data. From the point of view of the input and output of the system, the input is one or more data related to human behavior, and the data is a time series obtained by different sensors through sampling at a certain frequency. The output of the system is the recognition and classification result of human behavior. Generally speaking, the input of human action recognition system is divided into four forms of data: RGB time series, skeleton time series, depth map video and infrared video. With the rapid development of depth sensors, the acquisition of skeleton data is becoming more and more convenient and f...

AI Technical Summary

Problems solved by technology

Specifically, LSTM is used to extract the discriminative features of video sequences under a single view, thus ignoring the connection between videos of the same behavior under multiple views; at the same time, each joint point in the multi-view skeleton data, each frame and each Views have different effects on angle-independent skeleton behavior recognition, but only through LSTM modeling methods, different composition structures of skeleton data have the same contribution to behavior recognition, which limits the accuracy of angle-independent skeleton behavior recognition

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