Human body behavior identification method based on Bi-LSTM-Attention model

A recognition method and model technology, used in character and pattern recognition, neural learning methods, biological neural network models, etc., can solve problems such as easy underfitting, inability to effectively learn motion time series features, and difficulty in optimizing network models due to gradient dissipation. , to achieve good performance

Inactive Publication Date: 2019-05-21
JIANGNAN UNIV
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Problems solved by technology

However, the depth of CNN has a great influence on the feature extraction of video frames: the low network level is not easy to show the deep features of the image, and it is easy to underfit; the deep netw

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  • Human body behavior identification method based on Bi-LSTM-Attention model
  • Human body behavior identification method based on Bi-LSTM-Attention model
  • Human body behavior identification method based on Bi-LSTM-Attention model

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Embodiment Construction

[0037] The present invention will be further described below in conjunction with specific drawings and embodiments.

[0038] The present invention proposes a human action recognition method based on Bi-LSTM-Attention model (One HumanAction Recognition Algorithm Based on Bi-LSTM-Attention model);

[0039]The method first extracts 20 video frames from each video, extracts the depth features of the video frames through the InceptionV3 model, and then constructs the feature vectors in the forward and backward Bi-LSTM neural networks, and then uses the attention (Attention) mechanism The model adaptively perceives the network weights that have a greater impact on the recognition results, so that the Bi-LSTM-Attention model can achieve more accurate recognition according to the context of the behavior, and finally classify the video through a fully connected layer connected to the softmax classifier .

[0040] The method mainly includes three steps:

[0041] Step S1, input the ext...

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Abstract

The invention provides a human body behavior identification method based on a Bi-LSTM-Attention model. The method comprises the following steps: step S1, inputting an extracted video frame into an Inception V3 model, using the Inception V3 model to increase the depth of a convolutional neural network and reduce network parameters at the same time, and fully extracting depth features of the video frame to obtain related feature vectors; step S2, transmitting the feature vector obtained in the step S1 to Bi-LSTM neural network for processing, fully learning time sequence characteristics betweenthe video frames through the BI-LSTM neural network; and step S3, transmitting the time sequence feature vectors obtained in the step S2 to an attention mechanism model to adaptively perceive the network weight which has a great influence on the identification result, so that the features related to the network weight can get more attention. The recognition rate of human body behaviors can be improved.

Description

technical field [0001] The invention relates to the field of video analysis and recognition, in particular to a human behavior recognition method based on a Bi-LSTM-Attention model. Background technique [0002] For human behavior recognition, in the early days, most of them used artificially designed methods to extract video features. One scheme uses the method of spatio-temporal interest points to extract human body features in a complex background. This method calculates the strength of each position in the video sequence and finds spatio-temporal interest points through the method of maximum value filtering. WANG W et al. use the method of sparse coding to learn static features, and use the time-domain pyramid structure based on maximum pooling to represent the features in a histogram, and finally use SVM for classification. Another scheme proposes a hierarchical clustering multi-task learning (HC-MTL) approach to realize human action recognition by enforcing shared act...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62G06N3/04G06N3/08
Inventor 卢先领朱铭康王骏
Owner JIANGNAN UNIV
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