Gesture recognition method for optimizing projection symmetry approximate sparse classification

A gesture recognition and sparse classification technology, which is applied in the field of OP-SYSRC robust gesture recognition algorithm based on big data, can solve the problems of low recognition rate and poor real-time performance of gesture recognition, and achieves improved classification effect, fast and accurate gesture recognition, stability, The effect of ensuring data scalability

Active Publication Date: 2019-09-06
NORTHWESTERN POLYTECHNICAL UNIV
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Problems solved by technology

[0003] Poor real-time performance and low recognition rate of gesture recognition for big data

Method used

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  • Gesture recognition method for optimizing projection symmetry approximate sparse classification
  • Gesture recognition method for optimizing projection symmetry approximate sparse classification
  • Gesture recognition method for optimizing projection symmetry approximate sparse classification

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Embodiment Construction

[0041] The present invention proposes an OP-SYSRC robust gesture recognition algorithm based on big data, which mainly consists of three parts: technical features of gesture recognition, construction of OP-SYSRC algorithm, and model training / testing.

[0042] The method specifically includes steps as follows:

[0043] 1. Feature extraction for gesture recognition:

[0044] After collecting a lot of gesture image data, first of all, the gesture image needs to be normalized and corrected, which is an important part of gesture recognition. Its purpose is mainly to remove distracting information such as complex backgrounds in gesture images. The result of normalization correction directly affects the effect of subsequent feature extraction and classification recognition. This allows each image to contain only the characteristic regions of the gesture.

[0045] After the normalized gesture image is obtained, the POEM features of the image are extracted, and feature size reductio...

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Abstract

The invention relates to a gesture recognition method for optimizing projection symmetry approximate sparse classification, and the method comprises the steps: firstly, carrying out the normalizationcorrection of a gesture image, extracting gesture features, and building an OP-SYSRC algorithm and then adopting Faster R-CNN algorithm of target identification and using transfer learning to construct a gesture recognition model, and adding the constructed OP-SYSRC algorithm after region selection and feature extraction programs are carried out on the model for training and testing the model. Under the big data gesture recognition environment, the recognition and contrast performance of gesture images under the large database capacity is greatly improved, and the data expandability of the system is ensured.

Description

technical field [0001] The invention belongs to the field of image processing and computer vision, and in particular relates to a big data-based OP-SYSRC (Optimized Projective Symmetry Approximate Sparse Representation Classification) robust gesture recognition algorithm. Background technique [0002] The development and application of big data technology has promoted the rapid development of information technology in various fields, and a large amount of data is constantly updated to support various databases. If these data are put to good use, they will deliver a huge return in value. Gesture recognition has been widely used in personal authentication, video surveillance and human-computer interaction and other fields. At present, gesture recognition has been gradually applied to the work of commanding sign language in the military. The commander makes command gestures of the troops to the computer equipment, and the computer detects and understands the command gestures ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/28G06F18/21345G06F18/241
Inventor 黄攀峰李沅澔董刚奇马志强陈路
Owner NORTHWESTERN POLYTECHNICAL UNIV
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