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A human body behavior identification method and system based on multi-feature linear time sequence coding

A multi-feature, linear technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of ignoring the ability of behavioral representation of motion boundary graphs, and unsolved behavioral convolution feature time series feature integration learning and other problems

Pending Publication Date: 2019-04-16
JINGCHU UNIV OF TECH
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Problems solved by technology

However, the existing technical solutions ignore the behavioral representation ability of the motion boundary map, and do not solve the problem of integrated learning of behavioral convolution features and temporal features

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  • A human body behavior identification method and system based on multi-feature linear time sequence coding

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[0031] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0032] The method of the invention is divided into two stages of training and behavior recognition. In the training phase, training samples are used to train the weight parameters of the CNN. In the behavior recognition stage, the trained CNN network and linear time sequence coding method are used to extract behavior features, and the behavior classification results are given based on the behavior features.

[0033] see figure 1 , a kind of human behavior recognition method based on multi-feature linear time series coding provided by the present invention, ...

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Abstract

The invention discloses a human body behavior identification method and system based on multi-feature linear time sequence coding, and the method comprises the steps: firstly, uniformly dividing an original video into a plurality of sub-segments, constructing a continuous frame set in each sub-segment, and respectively calculating the optical flow between two adjacent image frames in each sub-segment continuous frame set, and obtaining an optical flow image set; Calculating a motion boundary value to obtain a motion boundary image set; Taking a representative frame, an optical flow image set and a motion boundary image set of the original video as input, and obtaining CNN characteristics of each sub-segment of the original video by adopting a CNN network; Performing linear time sequence coding according to the CNN features of different modalities; Respectively carrying out action recognition by adopting an action classification algorithm according to the linear time sequence coding characteristics; And carrying out weighted fusion on the plurality of behavior recognition results to obtain a final behavior recognition result. According to the method, the important behavior representation information of the motion boundary graph is fused, and the integrated learning process of the behavior convolution features and the time sequence features is fused through the linear time sequence coding method, so that the human body behavior recognition accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of automatic video analysis, and relates to a human behavior recognition method and system, in particular to a human behavior recognition method and system based on multi-feature linear time sequence coding. Background technique [0002] Human behavior recognition technology can meet the automatic analysis and intelligence requirements of intelligent video surveillance analysis, intelligent video monitoring and other application fields, and has very important practical significance in improving the level of smart city construction and promoting the development of smart medical care. At present, research on human behavior recognition technology mainly focuses on behavior recognition based on deep learning, including two aspects of learning: behavior convolution features and behavior timing features. The former uses the convolutional neural network to learn local depth features of human behavior from different...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/20G06F18/214
Inventor 陈华锋高正明赵运红贺体刚胡秀
Owner JINGCHU UNIV OF TECH
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