Human body behavior recognition method based on global characteristics and sparse representation classification

Abstract

The invention relates to a human body behavior recognition method based on global characteristics and sparse representation classification. The method comprises the following steps: performing Gaussian kernel convolutional filtering preprocessing on a video frame, and extracting a moving foreground pixel by using a differential method; sampling a pixel value according to a time space dimension ofa parameter, determining a moving area, adjusting the size of the video frame, performing primary dimension reduction, splicing video frames in rows to form a vector group, and acquiring characteristic vectors; splicing the characteristic vectors in rows to form a characteristic matrix, performing secondary dimension reduction, calculating a primary characteristic dictionary of the characteristicmatrix, initializing the dictionary, after dictionary initialization, performing dictionary learning by using a class accordant K-time matrix singular value decomposition method, calculating an inputsignal sparse code according to the dictionary, inputting the code into a classifier, and outputting a behavior type; and counting dictionary learning parameters, and performing behavior recognition in real time. By adopting the method, dictionaries and linear classifiers with both reconstitution functions and classification functions are acquired, human body behavior recognition efficiency is improved, and the method is applicable to scientific fields such as security monitoring, video search based on contents and virtual reality.

Description

technical field [0001] The invention relates to the technical field of video surveillance images and video processing, in particular to a human behavior recognition method based on global features and sparse representation classification. Background technique [0002] In recent years, with the rapid development of intelligent video surveillance systems, moving object detection, as an important link in intelligent video surveillance systems, has become a hot research issue in current computer vision. Moving object detection is to extract the foreground moving area from the background image in the video sequence frame. In an intelligent video surveillance system, the effect of moving target detection plays a decisive role in later target tracking, behavior understanding, and target classification. At present, video moving target detection methods mainly include optical flow method, inter-frame difference method, background subtraction method, etc. Among them, background subtr...

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Problems solved by technology

[0005] At present, there are some problems in the field of human behavior recognition, such as the complexity of the scene in the video, the intra-class difference of behavior, the inter-class similarity of behavior and the lack of comprehensive and accurate motion feature representation, which leads to the fact that in the actual complex environment, the feature representation is vulnerable. Influenced by changes in the external environment, the classification model has poor anti-interference ability, and the accuracy of human behavior recognition is low

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