Method for detecting human motion characteristics in cluttered background

Abstract

The invention discloses a method for detecting human motion characteristics in a cluttered background. The method comprises a step of using a salient point detection method based on information entropy proposed by Kadir to detect a spatial domain interest point in a spatial domain, a step of carrying out background interest point suppression on the detected spatial domain interest point, a step of using a 1DGabor filter to filter in a time domain and obtaining candidate spatial-temporal characteristic points, a step of carrying out non-maximum suppression processing on a spatial-temporal characteristic point response function, and allowing a point at a response function local maximum as a final characteristic point. According to the method, an interest point detection method based on the information entropy is employed, thus more rich characteristic points can be detected, through suppressing a background interest point, most detected interest points are in a human body, the accuracy of characteristic point detection is effectively improved, and a guarantee is provided for the subsequent correct recognition of human motion.

Description

technical field [0001] The invention relates to the technical field of machine vision, in particular to a method for detecting human action features in a cluttered background. Background technique [0002] Vision-based human action recognition technology is an important research topic in video understanding. It has far-reaching theoretical research significance and wide application in the fields of intelligent video surveillance, content-based video annotation and retrieval, intelligent human-computer interaction interface and animation synthesis. prospect. [0003] At present, the recognition of simple human actions in simple scenes has made some progress, but the recognition of human actions in complex scenes still faces many difficulties. In the real environment, due to the influence of factors such as cluttered background, occlusion and illumination changes, the accuracy of human action recognition is not high. Common features can be divided into static features, dynam...

AI Technical Summary

Problems solved by technology

[0004] Although spatio-temporal features are a sparse feature detection method, too few feature points will lose important motion information, which will affect the accuracy of behavior recognition in subsequent steps

When performing feature detection on human actions in a cluttered background, most of the feature points detected by most spatio-temporal feature detection methods are located on the background, and the false detection rate is high.

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