Behavior recognition method based on action subspace and weight behavior recognition model

A recognition method and recognition model technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve problems such as difficult to adapt to a variety of complex features such as long-distance dependencies

Inactive Publication Date: 2013-02-20
GUANGXI UNIVERSITY OF TECHNOLOGY
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AI Technical Summary

Problems solved by technology

However, this generative model usually uses a strong independence assumption, which makes it difficult to adapt to multiple complex features or long-range dependencies in observations

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  • Behavior recognition method based on action subspace and weight behavior recognition model
  • Behavior recognition method based on action subspace and weight behavior recognition model
  • Behavior recognition method based on action subspace and weight behavior recognition model

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Embodiment Construction

[0050] The present invention will be described in detail below in conjunction with specific embodiments.

[0051] 1.1 Behavior recognition

[0052] The present invention proposes as figure 1 A comprehensive probabilistic framework for action recognition is shown, which consists of two modules: feature extraction and description in high-dimensional image space, and behavior modeling and recognition in low-dimensional embedding space.

[0053] Training process: Input the behavior video sequence to be trained, use the dynamic background detection method or the static background detection method to detect the background image, use the background subtraction method to obtain the foreground image; use the method introduced in 1.2.1 to extract the contour of the moving target and correct the contour The features are represented accordingly, and the contour features can be expressed as block features of different sizes; the KPCA algorithm is used to achieve nonlinear dimensionality r...

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Abstract

The invention discloses a behavior recognition method based on an action subspace and weight behavior recognition model. The behavior recognition method includes the following steps: A1, inputting a behavior video sequence to be tested, using a dynamic background detection method or a static background detection method for detecting background images, and using a background reduction method for obtaining foreground images; A2, extracting an outline of a moving object, and expressing outline characteristics correspondingly; A3, using a kernel principal component analysis (KPCA) algorithm for achieving nonlinear dimensionality reduction on high-dimensional characteristics in kernel guide subspace, and mapping behavior track in a low-dimensional space; and A4, using the behavior recognition model namely a world cancer research fund (WCRF) module for performing behavior recognition. An experimental result shows that a provided framework can recognize human behavior of personnel changes inside and outside an area along the time accurately and is strong in robustness of noise and other influencing factors.

Description

technical field [0001] The invention relates to a behavior recognition method based on an action subspace and a weighted behavior recognition model. Background technique [0002] Human action recognition has broad application prospects, such as video surveillance and monitoring, object video summarization, intelligent interface, human-computer interaction, sports video analysis, video retrieval, etc. It has attracted the attention of more and more computer vision researchers. In general, action recognition involves two important issues. One is how to extract useful motion information from raw video data, and the other is how to build a motion reference model so that training and recognition methods can effectively deal with intra-class similar behaviors with varying spatial and temporal scales. [0003] Action recognition can utilize various cues such as key poses, optical flow, local descriptors, motion trajectories or feature tracking, contours, etc. But using keyframes...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
Inventor 王智文刘美珍夏冬雪蔡启先李绍滋唐新来罗功坤阳树洪廖志高
Owner GUANGXI UNIVERSITY OF TECHNOLOGY
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