A behavior recognition method based on deep learning

A recognition method and deep learning technology, applied in the field of behavior recognition, can solve problems such as increased system energy consumption and lack of objectivity in the selection of recognition model feature values, achieving the effects of reducing time, reducing training time, and controlling complexity

Active Publication Date: 2019-06-14
NANJING UNIV OF POSTS & TELECOMM
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AI Technical Summary

Problems solved by technology

[0002] At present, the existing behavior recognition based on machine learning often needs to manually extract a large number of feature values, which leads to the lack of objectivity of the recognition model in the selection of feature values ​​and the increase of system energy consumption.

Method used

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  • A behavior recognition method based on deep learning
  • A behavior recognition method based on deep learning
  • A behavior recognition method based on deep learning

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Embodiment Construction

[0037] The present invention will be further described in detail below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solutions of the present invention more clearly, but not to limit the protection scope of the present invention.

[0038] figure 1 It is a schematic flow chart of the overall method of the present invention; as figure 1 As shown, the behavior recognition method provided in this embodiment is divided into a training phase and a recognition phase.

[0039] The training phase mainly includes four parts, which are sensor data acquisition and preprocessing, STFT transformation, establishment of LSTM-DRNN behavior recognition model, and training to obtain model parameters. Its specific process is as figure 2 shown.

[0040] The training phase of the LSTM-DRNN model specifically includes the following steps:

[0041] Step 1: Use the acceleration sensor to collect the behavior data of the x-axis, y-...

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Abstract

The invention discloses a behavior recognition method based on deep learning. The method is characterized by dividing the behavior recognition into a training stage and a recognition stage, at the training phase, preprocessing the triaxial acceleration data acquired by an acceleration sensor and extracting a resultant acceleration value; obtaining a root-mean-square value of quadratic sum of three-axis acceleration of xyz, performing STFT on a resultant acceleration value to extract a relationship between time and frequency in data, and inputting an energy spectrum of an STFT sequence and a behavior tag corresponding to each collected data as a training set of a behavior recognition model; and enabling the behavior recognition model to adopt a DRNN based on an LSTM unit, training the initial model by using training set data, and selecting the training model with the highest test set classification accuracy to be applied to the recognition stage. Compared with an existing scheme, the method is higher in recognition accuracy, lower in power consumption and suitable for working at an intelligent terminal with limited resources.

Description

technical field [0001] The invention relates to a behavior recognition method based on deep learning, which belongs to the field of behavior recognition. Background technique [0002] At present, the existing behavior recognition based on machine learning often needs to manually extract a large number of feature values, resulting in the lack of objectivity of the recognition model in the selection of feature values ​​and the increase of system energy consumption. The application of deep learning algorithms can avoid the process of manually extracting feature values, and the recognition model can independently find features from the input data, so as to predict the results and ensure the accuracy of the system. The convenience and efficiency of deep learning algorithms make their application in the field of behavior recognition a research hotspot. [0003] In the method of behavior recognition using deep learning, it is an important aspect of research to improve the accuracy...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00
CPCY02D10/00
Inventor 王玉峰李潇
Owner NANJING UNIV OF POSTS & TELECOMM
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