Behavior recognition method of nuclear covariance descriptors based on dense tracks

Abstract

The invention discloses a behavior recognition method of nuclear covariance descriptors based on dense tracks. The objective of the invention is to solve a problem of low behavior recognition accuracy caused by nonlinear correlation between different characteristics which are not considered in the prior art. The method comprises steps of 1), extracting dense tracks, extracting characteristics of each pixel point in a track cuboid and acquiring a base layer characteristic matrix; 2), calculating a nuclear covariance matrix of the base layer characteristic matrix, mapping the nuclear covariance matrix to the euclidean space and acquiring vectorization characteristic representation; 3), by use of all characteristic representation in the track cuboid and constructing nuclear covariance descriptors based on dense tracks; and 4), using a BOW model to code the nuclear covariance matrix descriptors, acquiring a codon histogram, training a SVM by use of the codon histogram of a training set, testing the codon histogram of the training set in the trained SVM and acquiring a behavior recognition result. According to the invention, description ability of behaviors is further improved and the method can be used in complex environment like video monitoring.

Description

technical field [0001] The invention belongs to the technical field of video processing, and in particular relates to a behavior recognition method, which can be used to describe video behavior in complex video monitoring environments. Background technique [0002] The wide application of video behavior recognition in the fields of human-computer interaction, virtual reality, video surveillance, and video retrieval and analysis has aroused the interest of more and more researchers, and has important academic research value and strong practical value. In the field of action recognition, the existence of factors such as viewing angle changes and complex backgrounds increases the difficulty of action recognition. In this case, due to the robustness of artificial local features to video noise, illumination changes and complex backgrounds, it has become a important research directions. At present, the most popular artificial local features are gradient direction histogram HOG ba...

AI Technical Summary

Problems solved by technology

Although this method takes into account the relationship between different features, it only considers the static features and does not consider the dynamic features of the behavior subject, resulting in low accuracy of behavior recognition

[0005] (2) Yi Y, Wang H. Motion keypoint trajectory and covariance descriptor for human action recognition [J]. The Visual Computer, 2017: 1-13, th

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