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Gesture recognition method based on improved residual neural network

A gesture recognition and neural network technology, applied in the field of design image processing, can solve problems such as network accuracy and gradient dispersion, achieve high universality, improve classification accuracy, and ensure robustness

Inactive Publication Date: 2018-05-18
SOUTH CHINA UNIV OF TECH
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Problems solved by technology

[0009] The purpose of the present invention is to solve the above-mentioned defects in the prior art, provide a gesture recognition method based on the improved residual neural network, to solve the problems of network accuracy and gradient dispersion, and greatly improve the accuracy of gesture recognition

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  • Gesture recognition method based on improved residual neural network

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Embodiment

[0046] This embodiment discloses a gesture recognition method based on the improved residual neural network. For the specific flow chart, refer to the attached figure 1 shown, including the following steps:

[0047]S1. Acquisition of a training sample set. The design of the gesture sample data set refers to the CIFAR-10 data set. The design principle is: increase the intra-class variation and reduce the inter-class variation. In order to increase the intra-class variation and ensure that the model can still accurately recognize gestures under different angles, lighting, and backgrounds, the dataset collects gesture samples under multiple lighting, multiple angles, and multiple backgrounds, as shown in Figure 3(a) and Figure 3 (b) and Figure 3(c) show gesture samples collected under different lighting, angles, and backgrounds. In order to reduce the difference between classes, different gesture designs are kept as small as possible, as shown in Figure 3(d) for the definitions...

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Abstract

The invention discloses a gesture recognition method based on an improved residual neural network. The method includes the following steps: S1, acquisition of a training sample set; S2, preprocessingon the training sample set, wherein positions of gestures in images are found through algorithms, and cropped images are used as original training data; S3, enhancement of training samples, wherein translation transformation, rotation transformation, mirror-image transformation, scaling transformation and the like are carried out on the collected training samples to enlarge the training sample set; S4, acquisition of a gesture model, wherein a processed training sample set is input into the pretrained residual network to carry out training on network parameters to obtain the gesture recognition model; S5, a step of carrying out processing, which is the same as the step S2, on to-be-recognized gesture images to obtain to-be-recognized gesture data; and S6, a step of inputting the to-be-recognized gesture data into the network, of which training is completed, to obtain a gesture sequence. The method is based on the deep residual network, trains the residual network on the self-collecteddata set, and realizes high-recognition-rate gesture recognition of a third view angle.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a gesture recognition method based on an improved residual network. Background technique [0002] Regarding gesture recognition, as an important extension of human-computer interaction, gesture recognition is widely welcomed because of its intuitive, natural and easy-to-learn characteristics, and has a very wide range of applications, such as optimizing game experience in game interaction, traffic gesture recognition in automatic driving, Automated sign language interpretation facilitates people with disabilities. [0003] Gesture recognition systems can be divided into 3 categories: [0004] 1. Recognition based on the data collected by the data glove. Under the data collected by the data glove, Miguel Simao et al. used bicubic interpolation based on the ANN method to solve the problem of the same model for static gestures and dynamic gestures, reaching 98.7% of static...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62
CPCG06V40/28G06F18/241
Inventor 张鑫林宏辉李晨阳郑浩东
Owner SOUTH CHINA UNIV OF TECH
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