Behavior recognition method based on intelligent sub-space networks

Abstract

The invention discloses a behavior recognition method based on intelligent sub-space networks. The method specifically includes the following steps of preprocessing and learning spatial characteristics, extracting spatial and temporal characteristics, representing videos as histogram vectors, training SVM classifiers and recognizing behaviors. According to the behavior recognition method based on the intelligent sub-space networks, the spatial characteristics are extracted from video data, the spatial characteristics are then pooled so that the characteristics equivalent to the spatial and temporal characteristics can be obtained, each video sequence is represented as the corresponding histogram vector about all words in a word bag through a word bag method, multiple types of behaviors are recognized through the trained SVM classifiers and a one-to-many strategy, the robustness of behavior recognition is increased, and the behavior recognition rate is increased.

Description

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AI Technical Summary

Benefits of technology

This technology allows for better understanding how people behave by analyzing videos with their own brain activity or other senses like touch sense over time. It can use this knowledge to learn more accurate behaviors through learning patterns within different parts of an image called space (called vox). These models help identify specific types of movement without relying solely upon previous movements themselves. Overall, it improves performance and efficiency in identifying complex human actions such as turning off lights at home during sleep hours.

Problems solved by technology

Technological Problem addressed in this patented technical solution describes how current state-of-art techniques involve analyzing visual characteristics like color intensity distribution patterns, histogram representations, and binary pattern recognition algorithms, which cannot effectively recognize dynamic changes caused by external factors like light sources or noise levels during natural phenomena.

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