Video behavior identification method based on an Attention-LSTM network

A recognition method and network technology, applied in the field of computer vision, can solve problems such as low time efficiency and time-consuming action positioning, and achieve the effect of improving accuracy

Active Publication Date: 2019-05-10
SOUTHEAST UNIV +2
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AI Technical Summary

Problems solved by technology

In the past, the behavior detection method mostly used the sliding window method, but the action positioning based on the sliding window method is very time-consuming and time-efficient

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  • Video behavior identification method based on an Attention-LSTM network
  • Video behavior identification method based on an Attention-LSTM network
  • Video behavior identification method based on an Attention-LSTM network

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Embodiment Construction

[0037] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0038] A video behavior recognition method based on Attention-LSTM network, such as figure 1shown. First, the input RGB image sequence is transformed by the optical flow image sequence generation module to obtain the optical flow image sequence; secondly, the obtained optical flow image sequence and the original RGB image sequence are input into the temporal attention frame acquisition module, and two The non-redundant key frames in the image sequence; then, input the key frame sequences of the two types of images into the AlexNet network feature extraction module, and extract the temporal and spatial features of the two frame images respectively. At the same time, in the last stage of the AlexNet network Between the convolutional layer and the fully connected layer, the feature decentralization module is used to strengthen the feature map...

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Abstract

The invention discloses a video behavior identification method based on an Attention-LSTM network. The method includes transforming the input RGB image sequence through an optical flow image sequencegeneration module to obtain an optical flow image sequence; inputting the optical flow graph sequence and the original RGB graph sequence into a time domain attention frame taking module, and respectively selecting non-redundant key frames in the two graph sequences; inputting the key frame sequences of the two images into an AlexNet network feature extraction module, respectively extracting timesequence features and spatial features of the two frame images, and performing a feature weight increasing operation with strong action correlation on the feature image output by the last convolutional layer through a feature weight increasing module; and inputting the feature maps output by the two AlexNet network feature extraction modules into an LSTM network behavior identification module, respectively identifying the two pictures, and fusing the two identification results in proportion through a fusion module to obtain a final video behavior identification result. According to the invention, the function of identifying the behavior from the video can be realized, and the identification accuracy can be improved.

Description

technical field [0001] The invention belongs to the field of computer vision, and in particular relates to a video behavior recognition method based on an Attention-LSTM network. Background technique [0002] Intelligent video analysis is currently a very hot and challenging direction in the field of computer vision. The direction of intelligent video analysis includes many sub-research directions, and the main two research directions are behavior recognition and behavior detection. Behavior recognition is similar to image classification. It mainly solves the problem of "what is the behavior in the video". Given a trimmed video containing only one behavior, it is required to classify the video. Behavior detection (or positioning) is consistent with target detection, and mainly solves the problem of "whether there is a corresponding behavior in the video, and if so, in which segment of the video frame sequence and where in each frame", which is mainly divided into two parts:...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06N3/04G06N3/08
Inventor 陆生礼庞伟向丽苹范雪梅舒程昊吴成路阮小千梁彪邹涛
Owner SOUTHEAST UNIV
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