time sequence behavior detection method based on 3D human body key points

A detection method and key point technology, applied in the field of time series behavior detection based on 3D human body key points, can solve problems such as the decline of time span detection effect

Inactive Publication Date: 2019-06-21
CHINA UNIV OF PETROLEUM (EAST CHINA)
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Problems solved by technology

However, in actual engineering scenes with severe human body occlusion, variable postures, and many distracting objects, the time span of time-series behavior fragments ma

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  • time sequence behavior detection method based on 3D human body key points
  • time sequence behavior detection method based on 3D human body key points
  • time sequence behavior detection method based on 3D human body key points

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Embodiment Construction

[0048] Below in conjunction with accompanying drawing and specific embodiment the present invention is described in further detail:

[0049] A time-series behavior detection method based on key points of 3D human body, such as figure 1 Shown is the flowchart of the time series behavior detection method based on 3D human body key points of the present invention, the method includes:

[0050] S1, data preprocessing, input an untrimmed video V, and convert the video data into continuous L frames of RGB images through video sequence preprocessing. In order to generate a random extraction frame, set a hash function calculation every 24 frames, select a random function random each time, take the frame number of each frame as its hash address, and obtain a randomly generated frame number, that is for extracting frames.

[0051] S2, target boundary detection, extracts features from single-frame static images through multiple convolution operations to obtain feature maps, in order to...

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Abstract

The invention discloses a time sequence behavior detection method based on 3D human body key points, and the method comprises the steps: taking video data as input, and converting the video data intocontinuous frame images through data preprocessing;c arrying out Feature extraction by using a multilayer CNN network, and detecting a boundary frame of a personnel target in the image; secondly, through body part positioning and correlation degree analysis, acquiring 2D human body key point coordinates and mapping from 2D human body key points to achieve 3D key points by constructing a key pointregression network; inputting the 3D joint coordinates into a space-time diagram convolutional network to carry out frame-level action recognition and classification on the whole video sequence, and grouping adjacent frames of the same label to obtain action proposal segments with different granularities; andprecisely correcting the time boundary of the action through fine-grained integrity filtering, so that the time sequence behavior detection in a complex scene is realized. According to the method, more valuable information can be analyzed from the 3D data, and the time sequence behavior detection and positioning precision is remarkably improved.

Description

technical field [0001] The invention belongs to the field of computer graphics and image processing, and relates to a time-series behavior detection method based on key points of a 3D human body. Background technique [0002] Understanding human motion and behavior in video is a challenging problem in the field of computer vision and intelligent video analysis, and it is also the key to understanding video content. It has a wide range of application prospects, and time-series behavior detection is an important research topic in this field. In recent years, due to the rapid growth of video volume and the rapid development of neural networks, temporal behavior detection has received more attention, and research in this area has made some progress. A video may contain one or more behavioral segments. In a given undivided long video, detect the behavioral segments in the video, including its start time, end time, and behavior category. This understanding and application of human...

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Application Information

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IPC IPC(8): G06K9/00G06K9/46G06K9/62
Inventor 宫法明马玉辉李昕袁向兵宫文娟
Owner CHINA UNIV OF PETROLEUM (EAST CHINA)
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