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Behavior feature extraction method, system based on space-time frequency domain hybrid learning, and device

A hybrid learning and feature extraction technology, applied in the field of behavior recognition, can solve the problem of low accuracy of behavior feature extraction, achieve the effect of improving feature extraction accuracy and reducing the number of layers and parameters

Active Publication Date: 2019-05-03
INST OF AUTOMATION CHINESE ACAD OF SCI
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Problems solved by technology

[0004] In order to solve the above-mentioned problems in the prior art, that is, in order to solve the problem of low accuracy of behavioral feature extraction, the present invention provides a behavioral feature extraction method based on time-space-frequency domain hybrid learning, including:

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[0058] The application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain related inventions, not to limit the invention. It should also be noted that, for the convenience of description, only the parts related to the related invention are shown in the drawings.

[0059] It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and embodiments.

[0060] Existing behavior recognition methods mainly use local networks with only local affinity fields stacked in the space-time domain to extract the spatio-temporal features of behavior sequences hierarchically, and then identify and detect behaviors. They are limited to mining spa...

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Abstract

The invention belongs to the field of behavior recognition, particularly relates to a behavior feature extraction method, system based on space-time frequency domain hybrid learning, and a device, andaims to solve the problem of low skeleton behavior feature extraction precision. The method comprises the steps of obtaining a video behavior sequence based on a skeleton, and extracting a time-spacedomain behavior feature map through converting a network; inputting the time-space domain behavior feature map into a frequency domain attention network, performing frequency selection, inverting toa time-space domain, and adding the obtained behavior feature map to the time-space domain behavior feature map; synchronously performing local and non-local reasoning, and performing high-level localreasoning; and globally pooling the time-space domain behavior feature map obtained through reasoning to obtain the behavior feature vector of the video behavior sequence. The method can be applied to behavior classification, behavior detection and the like. According to the method, an effective frequency mode is adaptively selected in a frequency domain, a network with local affinity fields andnon-local affinity fields is adopted in a time-space domain for space-time reasoning, local details and non-local semantic information can be synchronously mined, and therefore the behavior recognition precision is effectively improved.

Description

technical field [0001] The invention belongs to the field of behavior recognition, and in particular relates to a behavior feature extraction method, system and device based on time-space-frequency domain hybrid learning. Background technique [0002] Behavior recognition has a wide range of applications in the fields of intelligent monitoring, human-computer interaction, and automatic driving. Behavior recognition includes behavior classification and behavior detection. Classify, locate and detect it. Skeleton-based behavior recognition has aroused widespread interest in academia and industry in recent years due to its low computational overhead, concise representation, and robustness to changes in the environment and appearance. Specifically, skeletal behavior recognition is based on video sequences composed of 2D or 3D coordinates of the joint points of the target object in the environment to realize behavior recognition. [0003] Existing skeletal behavior recognition ...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/46G06N3/04G06N3/08
CPCG06N3/04G06N3/08G06V10/40
Inventor 胡古月崔波余山
Owner INST OF AUTOMATION CHINESE ACAD OF SCI
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