Video Human Behavior Recognition Method Based on Sparse Subspace Clustering

Abstract

The invention belongs to computer vision pattern recognition and video image processing methods, including: establishing a three-dimensional space-time sub-frame cube in the model of establishing video human behavior recognition, establishing human behavior feature space, clustering processing, updating labels, and establishing video human behavior recognition In the recognition of models and human behaviors, extract three-dimensional space-time sub-frame cubes from surveillance videos, extract human behavior features, determine the categories of human sub-behaviors in each video, and classify and merge videos with sub-category labels; and the current international understanding of Hollywood2 human behavior Compared with the highest recognition accuracy of the database, it has increased by 16.5%. Therefore, the invention has the ability to automatically extract more discriminative, adaptive, universal and invariant human behavior characteristics, reduces the over-fitting phenomenon and gradient diffusion problems in the neural network, and effectively improves human behavior in complex environments. The accuracy of recognition can be widely used in on-site video surveillance and video content retrieval.

Description

technical field [0001] The invention belongs to computer vision pattern recognition and video image processing methods, in particular to a neural network based on deep learning that adopts sparse subspace (SSC) clustering and subdivision and splits a large number of layers into several layers Fewer shallower approaches to video action recognition based on deep learning neural networks. Background technique [0002] Human behavior recognition based on video is a hot issue in the field of computer vision in recent years. As a typical video understanding problem, human behavior patterns can be identified and determined by analyzing the characteristics of human motion in video image sequences. More specifically, it extracts feature information that can describe behavior from video image sequences, uses machine learning and other technologies to understand it, and uses classifiers to classify to achieve the purpose of recognizing human behavior. [0003] With the development of ...

AI Technical Summary

Problems solved by technology

Although this method has strong anti-interference ability to the specific direction of the human body, bone size, and spatial position, and has a certain degree of generalization ability, it can be applied to human behavior recognition in a more ideal environment, but it needs to use 3D technology with high cost. In addition, the algorithm of this method is relatively complex, and the effect of human behavior recognition in more complex scenes is still not ideal

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