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Video based behavior recognition method and device

A recognition method and behavior technology, applied in the field of computer science, can solve the problem of low recognition ability of complex behavior, and achieve the effect of enhancing the ability to recognize complex behavior

Active Publication Date: 2017-05-24
SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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Problems solved by technology

[0003] In view of this, the embodiment of the present invention provides a video-based behavior recognition method and device to solve the problem of low recognition ability of RNN-based behavior recognition technology for complex behavior in the prior art

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  • Video based behavior recognition method and device

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Embodiment Construction

[0016] In the following description, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present invention. It will be apparent, however, to one skilled in the art that the invention may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0017] The embodiment of the present invention provides a behavior recognition method based on a recursive spatiotemporal attention network. By introducing a spatiotemporal attention mechanism into the RNN structure, the RNN can autonomously learn a behavior closely related to the video frame behavior at the current moment from the global video range at each moment. In this way, the spatio-tempora...

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Abstract

The invention is applicable to the technical field of computer science, and provides a video based behavior recognition method and device. The video based behavior recognition method comprises the steps of extracting depth features of all sampling time video frames, wherein the depth features comprise high-level semantic features and detail features of behaviors in the sampling time video frames; acquiring spatial-temporal features used for expressing a behavior of the current time video frame based on the detail features of the sampling time video frames; inputting the spatial-temporal features and the high-level semantic features into an LSTM (Long Short Term Memory) model so as to perform behavior recognition on the current time video frame. The spatial-temporal features and the high-level semantic features are enabled to cooperate in a complementary manner so as to enhance the complex behavior recognition capacity of an RNN (Recurrent Neural Network).

Description

technical field [0001] The invention belongs to the technical field of computer science, and in particular relates to a video-based behavior recognition method and device. Background technique [0002] In recent years, Recurrent Neural Network (RNN) has been widely used for action recognition in videos due to its effective sequence modeling ability. In the existing technology, RNN uses the high-level semantic features of each frame of the video as input at each moment to perform sequence model training. However, using high-level semantic features usually ignores the detailed position information of complex behaviors, thus limiting the behavior of RNN. recognition ability. Contents of the invention [0003] In view of this, the embodiments of the present invention provide a video-based behavior recognition method and device to solve the problem of low ability to recognize complex behaviors of the RNN-based behavior recognition technology in the prior art. [0004] In the ...

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Application Information

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IPC IPC(8): G06K9/00G06N3/02
CPCG06N3/02G06V20/41
Inventor 乔宇杜文斌王亚立
Owner SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
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